This document summarizes a study that aims to detect Alzheimer's disease through neuroimaging and subsequently fuse magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. The study first detects Alzheimer's from MRI scans by segmenting gray and white matter and calculating their volume ratios. Image fusion is then performed on the MRI scan detected with Alzheimer's and a corresponding PET scan. The fusion combines the two scans into a single image containing structural and anatomical information to aid clinical diagnosis. The proposed method achieved good performance metrics for the fused image, with a peak signal-to-noise ratio of 60.6 dB, mean square error of 0.0176, and other favorable values, suggesting it yields more informative results than individual scans
The document proposes a pixel-level fusion approach using vision transformer for early detection of Alzheimer's disease from MRI and PET scans. It applies discrete wavelet transform to decompose MRI and PET images into frequency bands. It then uses a pre-trained VGG16 model to optimize the decomposition. The frequency bands are fused using inverse discrete wavelet transform. Finally, a pre-trained vision transformer model classifies the fused images and achieves 81.25% accuracy for AD/EMCI and AD/LMCI classification on MRI data and 93.75% accuracy on PET data, outperforming existing methods on PET data classification.
SEGMENTATION OF MULTIPLE SCLEROSIS LESION IN BRAIN MR IMAGES USING FUZZY C-MEANSijaia
Magnetic resonance images (MRI) play an important role in supporting and substituting clinical
information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In
this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit
the modification of properties of fuzzy -c means algorithms and the canny edge detection. By changing and
reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship
between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient
condition and clustering parameters, allowing identification of them as (local) minima of the objective
function.
A Survey On Identification Of Multiple Sclerosis Lesions From Brain MRIJanelle Martinez
This document summarizes several papers on using MRI and image analysis techniques to identify multiple sclerosis (MS) lesions in the brain. It discusses using metrics like fractional anisotropy and apparent diffusion coefficient from diffusion tensor imaging to analyze changes in normal-appearing white matter. It also reviews methods like texture analysis using gray-level co-occurrence matrices, convolutional neural networks, and phase and orientation analysis to segment and identify MS lesions in MRI scans. The goal is to characterize subtle microscopic changes in nerve fibers caused by MS through analysis of MRI textures and alignments to improve diagnosis and understanding of the disease.
This document discusses a proposed method for multi-type medical image fusion through discrete wavelet transform. It begins with an introduction explaining that image fusion combines multiple input images into a single high-quality image containing maximum information. It then reviews existing fusion techniques like IHS, PCA, and wavelet/frequency methods. The proposed method decomposes PET and MRI images using discrete wavelet transform, combines high and low frequency coefficients, and performs inverse DWT to generate a fused image with less color distortion and all structural information. It is evaluated using metrics like MSE, PSNR, AG and SD on normal and Alzheimer's brain images.
A Review On Methods For Feature Extraction And Classification For The Automat...Heather Strinden
This document reviews various feature extraction and classification methods that have been used for the automated detection of Alzheimer's disease from magnetic resonance imaging (MRI) scans. It summarizes several studies that used different feature extraction techniques like voxel-based, vertex-based, and region of interest-based methods. Popular classification algorithms discussed include support vector machines, linear discriminant analysis, Bayesian classifiers and artificial neural networks. The document concludes that selecting relevant features extracted from MRI scans can yield accurate classification of Alzheimer's disease.
An internet of things-based automatic brain tumor detection systemIJEECSIAES
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
An internet of things-based automatic brain tumor detection systemnooriasukmaningtyas
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
Hippocampus’s volume calculation on coronal slice’s for strengthening the dia...TELKOMNIKA JOURNAL
Alzheimer’s is one of the most common types of dementia in the world. Although not a contagious disease, this disease has many impacts, especially in socio-economic life. In diagnosing Alzheimer’s and using interview techniques, physical examination methods are also used, namely using an magnetic resonance imaging (MRI) machine to get a clear image of the patient’s brain condition, with a focus on the hippocampus and ventricular area. In this paper, we discuss the calculation of the volume of the hippocampus, especially the coronal slice, to provide information to doctors in making decisions on diagnosing the severity of Alzheimer’s. Using the basis of volume calculations, we made a 3D visualization reconstruction of the coronal hippocampus slice area in order to make it easier for doctors to analyze the condition of the hippocampus area, which in the end will be used as a recommendation in the classification of the severity of Alzheimer’s. Our experimental results show, the lower the severity, the bigger the volume, the more slices, and the longer the counting time.
The document proposes a pixel-level fusion approach using vision transformer for early detection of Alzheimer's disease from MRI and PET scans. It applies discrete wavelet transform to decompose MRI and PET images into frequency bands. It then uses a pre-trained VGG16 model to optimize the decomposition. The frequency bands are fused using inverse discrete wavelet transform. Finally, a pre-trained vision transformer model classifies the fused images and achieves 81.25% accuracy for AD/EMCI and AD/LMCI classification on MRI data and 93.75% accuracy on PET data, outperforming existing methods on PET data classification.
SEGMENTATION OF MULTIPLE SCLEROSIS LESION IN BRAIN MR IMAGES USING FUZZY C-MEANSijaia
Magnetic resonance images (MRI) play an important role in supporting and substituting clinical
information in the diagnosis of multiple sclerosis (MS) disease by presenting lesion in brain MR images. In
this paper, an algorithm for MS lesion segmentation from Brain MR Images has been presented. We revisit
the modification of properties of fuzzy -c means algorithms and the canny edge detection. By changing and
reformed fuzzy c-means clustering algorithms, and applying canny contraction principle, a relationship
between MS lesions and edge detection is established. For the special case of FCM, we derive a sufficient
condition and clustering parameters, allowing identification of them as (local) minima of the objective
function.
A Survey On Identification Of Multiple Sclerosis Lesions From Brain MRIJanelle Martinez
This document summarizes several papers on using MRI and image analysis techniques to identify multiple sclerosis (MS) lesions in the brain. It discusses using metrics like fractional anisotropy and apparent diffusion coefficient from diffusion tensor imaging to analyze changes in normal-appearing white matter. It also reviews methods like texture analysis using gray-level co-occurrence matrices, convolutional neural networks, and phase and orientation analysis to segment and identify MS lesions in MRI scans. The goal is to characterize subtle microscopic changes in nerve fibers caused by MS through analysis of MRI textures and alignments to improve diagnosis and understanding of the disease.
This document discusses a proposed method for multi-type medical image fusion through discrete wavelet transform. It begins with an introduction explaining that image fusion combines multiple input images into a single high-quality image containing maximum information. It then reviews existing fusion techniques like IHS, PCA, and wavelet/frequency methods. The proposed method decomposes PET and MRI images using discrete wavelet transform, combines high and low frequency coefficients, and performs inverse DWT to generate a fused image with less color distortion and all structural information. It is evaluated using metrics like MSE, PSNR, AG and SD on normal and Alzheimer's brain images.
A Review On Methods For Feature Extraction And Classification For The Automat...Heather Strinden
This document reviews various feature extraction and classification methods that have been used for the automated detection of Alzheimer's disease from magnetic resonance imaging (MRI) scans. It summarizes several studies that used different feature extraction techniques like voxel-based, vertex-based, and region of interest-based methods. Popular classification algorithms discussed include support vector machines, linear discriminant analysis, Bayesian classifiers and artificial neural networks. The document concludes that selecting relevant features extracted from MRI scans can yield accurate classification of Alzheimer's disease.
An internet of things-based automatic brain tumor detection systemIJEECSIAES
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
An internet of things-based automatic brain tumor detection systemnooriasukmaningtyas
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
Hippocampus’s volume calculation on coronal slice’s for strengthening the dia...TELKOMNIKA JOURNAL
Alzheimer’s is one of the most common types of dementia in the world. Although not a contagious disease, this disease has many impacts, especially in socio-economic life. In diagnosing Alzheimer’s and using interview techniques, physical examination methods are also used, namely using an magnetic resonance imaging (MRI) machine to get a clear image of the patient’s brain condition, with a focus on the hippocampus and ventricular area. In this paper, we discuss the calculation of the volume of the hippocampus, especially the coronal slice, to provide information to doctors in making decisions on diagnosing the severity of Alzheimer’s. Using the basis of volume calculations, we made a 3D visualization reconstruction of the coronal hippocampus slice area in order to make it easier for doctors to analyze the condition of the hippocampus area, which in the end will be used as a recommendation in the classification of the severity of Alzheimer’s. Our experimental results show, the lower the severity, the bigger the volume, the more slices, and the longer the counting time.
Survey on Segmentation Techniques for Spinal Cord ImagesIIRindia
Medical imaging is a technique which is used to expose the interior part of the body, to diagnose the diseases and to treat them as well. Different modalities are used to process the medical images. It helps the human specialists to make diagnosis ailments. In this paper, we surveyed segmentation on the spinal cord images using different techniques such as Data mining, Support vector machine, Neural Networks and Genetic Algorithm which are applied to find the disorders and syndromes affected in the spinal cord system. As a result, we have gained knowledge in an identified disarrays and ailments affected in lumbar vertebra, thoracolumbar vertebra and spinal canal. Finally how the Disc Similarity Index values are generated in each method is also analysed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2iaemedu
The document summarizes a proposed method for detecting brain tumors in MRI images in 4 steps:
1. Skull stripping and smoothing are performed to isolate the brain region.
2. Histogram thresholding is used to detect if the brain is normal or abnormal by comparing histograms of halves of the brain image.
3. For abnormal brains, a modified gradient vector flow (GVF) model is used to create a force field and detect the tumor contour.
4. The area of the tumor is then calculated. The method aims to minimize segmentation time by skipping segmentation for normal brains detected in Step 2. Validation with expert segmentation is performed.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2IAEME Publication
The document describes a new method for detecting and segmenting brain tumors in MRI images. It combines histogram thresholding, modified gradient vector flow, and morphological operators. Histogram thresholding is used to detect if the brain is normal or abnormal. If abnormal, modified GVF is used to segment the tumor contour. Otherwise, segmentation is skipped to minimize computation time. The method was tested on many MRI brain images and tumor detection and dimensions were validated against expert segmentation. It provides an efficient and computationally inexpensive approach for brain tumor detection and segmentation in MRI images.
Comparison of resting electroencephalogram coherence in patients with mild co...IJECEIAES
Mild cognitive impairment (MCI) was a condition beginning before more serious deterioration, leading to Alzheimer’s dementia (AD). MCI detection was needed to determine the patient's therapeutic management. Analysis of electroencephalogram (EEG) coherence is one of the modalities for MCI detection. Therefore, this study investigated the inter and intra-hemispheric coherence over 16 EEG channels in the frequency range of 1-30 Hz. The simulation results showed that most of the electrode pair coherence in MCI patients have decreased compared to normal elderly subjects. In inter hemisphere coherence, significant differences (p<0.05) were found in the FP1-FP2 electrode pairs. Meanwhile, significant differences (p<0.05) were found in almost all pre-frontal area connectivity of the intra-hemisphere coherence pairs. The electrode pairs were FP2-F4, FP2-T4, FP1-F3, FP1-F7, FP1-C3, FP1-T3, FP1-P3, FP1-T5, FP1-O1, F3-O1, and T3-T5. The decreased coherence in MCI patients showed the disconnection of cortical connections as a result of the death of the neurons. Furthermore, the coherence value can be used as a multimodal feature in normal elderly subjects and MCI. It is hoped that current studies may be considered for early detection of Alzheimer’s in a larger population.Mild cognitive impairment (MCI) was a condition beginning before more serious deterioration, leading to Alzheimer’s dementia (AD). MCI detection was needed to determine the patient's therapeutic management. Analysis of electroencephalogram (EEG) coherence is one of the modalities for MCI detection. Therefore, this study investigated the inter and intra-hemispheric coherence over 16 EEG channels in the frequency range of 1-30 Hz. The simulation results showed that most of the electrode pair coherence in MCI patients have decreased compared to normal elderly subjects. In inter hemisphere coherence, significant differences (p<0.05) were found in the FP1-FP2 electrode pairs. Meanwhile, significant differences (p<0.05) were found in almost all pre-frontal area connectivity of the intra-hemisphere coherence pairs. The electrode pairs were FP2-F4, FP2-T4, FP1-F3, FP1-F7, FP1-C3, FP1-T3, FP1-P3, FP1-T5, FP1-O1, F3-O1, and T3-T5. The decreased coherence in MCI patients showed the disconnection of cortical connections as a result of the death of the neurons. Furthermore, the coherence value can be used as a multimodal feature in normal elderly subjects and MCI. It is hoped that current studies may be considered for early detection of Alzheimer’s in a larger population.
The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools.
The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients.
In this study, we integrated a lightweight custom convolutional neural network
(CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal
fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused
database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology,
which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of
pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by
2-5%. In conclusion, a customized lightweight CNN model and nature-inspired
optimization techniques can significantly enhance progress detection, leading to
better biomedical research and patient care.
Classification of Abnormalities in Brain MRI Images Using PCA and SVMIJERA Editor
The impact of digital image processing is increasing by the day for its use in the medical and research areas. Medical image classification scheme has been on the increase in order to help physicians and medical practitioners in their evaluation and analysis of diseases. Several classification schemes such as Artificial Neural Network (ANN), Bayes Classification, Support Vector Machine (SVM) and K-Means Nearest Neighbor have been used. In this paper, we evaluate and compared the performance of SVM and PCA by analyzing diseased image of the brain (Alzheimer) and normal (MRI) brain. The results show that Principal Components Analysis outperforms the Support Vector Machine in terms of training time and recognition time.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...IJEECSIAES
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...nooriasukmaningtyas
The document describes a proposed model for MRI brain diagnosis using genetic algorithms, convolutional neural networks, and the Internet of Things. The model has eight steps: loading MRI images, enhancing images, applying discrete wavelet transform, evaluating images using entropy, applying genetic algorithm for registration, subtracting images and using CNN to classify results as normal or abnormal, and sending messages to patients using Arduino and GSM. The model was tested on 550 normal and 350 abnormal MRI images, achieving 98.8% accuracy in classification.
MULTIPLE SCLEROSIS DIAGNOSIS WITH FUZZY C-MEANScscpconf
Magnetic resonance imaging (MRI) can support and substitute clinical information in the
diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm
for MS lesion segmentation. We revisit the modification of properties of fuzzy c means
algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply
canny contraction principle, and establish a relationship between MS lesions and edge
detection. For the special case of FCM, we derive a sufficient condition for fixed lesions,
allowing identification of them as (local) minima of the objective function.
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
Efficient Brain Tumor Detection Using Wavelet TransformIJERA Editor
Brain tumor detection is a challenging task and its very important to analyze the structure of the tumor correctly so a automatic method is used now a days for the detection of the tumor. This method saves time as well as it reduces the error which occurs in the method of manual detection. In this paper the tumor is detected using wavelet transform. MRI is an important tool used in many fields of medicine and is capable of generating a detailed image of any part of the human body. The tumor is segmented from the MRI images, features are extracted and then the area of the tumor is determined. PNN can successfully handle the process of brain tumor classification
Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Proce...IJECEIAES
The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.
IRJET - Fusion of CT and MRI for the Detection of Brain Tumor by SWT and Prob...IRJET Journal
This document summarizes a research paper that proposes a method for detecting brain tumors by fusing CT and MRI images using stationary wavelet transform and a probabilistic neural network classifier. The proposed method involves preprocessing the CT and MRI images using median filtering for noise removal. It then applies stationary wavelet transform to the images to extract features before segmenting the tumor region using k-means clustering. Finally, the probabilistic neural network classifier determines if the tumor is benign or malignant based on the fused image features. The paper reviews other existing fusion and classification methods and argues that the proposed stationary wavelet transform and probabilistic neural network approach provides better detection of brain tumors.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
A modified residual network for detection and classification of Alzheimer’s ...IJECEIAES
Alzheimer's disease (AD) is a brain disease that significantly declines a person's ability to remember and behave normally. By applying several approaches to distinguish between various stages of AD, neuroimaging data has been used to extract different patterns associated with various phases of AD. However, because the brain patterns of older adults and those in different phases are similar, researchers have had difficulty classifying them. In this paper, the 50-layer residual neural network (ResNet) is modified by adding extra convolution layers to make the extracted features more diverse. Besides, the activation function (ReLU) was replaced with (Leaky ReLU) because ReLU takes the negative parts of its input, drops them to zero, and retains the positive parts. These negative inputs may contain useful feature information that could aid in the development of high-level discriminative features. Thus, Leaky ReLU was used instead of ReLU to prevent any potential loss of input information. In order to train the network from scratch without encountering the issue of overfitting, we added a dropout layer before the fully connected layer. The proposed method successfully classified the four stages of AD with an accuracy of 97.49 % and 98 % for precision, recall, and f1-score.
Evaluation of Default Mode Network In Mild Cognitive Impairment and Alzheimer...CSCJournals
Although progressive functional brain network disorders has been one of the indication of Alzheimer's disease, The current research on aging and dementia focus on diagnostics of the cognitive changes of normal aging and Alzheimer Disease (AD), these changes known as Mild Cognitive Impairment (MCI). The default mode network (DMN) is a network of interacting brain regions known to have activity highly correlated with each other and distinct from other networks in the brain, the default mode network is active during passive rest and consists of a set of brain areas that are tightly functionally connected and distinct from other systems within the brain. Anatomically, the DMN includes the posterior cingulated cortex (PCC), dorsal and ventral medial prefrontal cortex, the lateral parietal cortex, and the medial temporal lobes. DMN involves multiple anatomical networks that converge on cortical hubs, such as the PCC, ventral medial prefrontal, and inferior parietal cortices. The aim of this study was to evaluate the default mode network functional connectivity in MCI patients. While no treatments are recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers and deserves more recognition and further study, In order to increase the ability to recognize earlier symptoms of Alzheimer's disease.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2iaemedu
The document summarizes a proposed method for detecting brain tumors in MRI images in 4 steps:
1. Skull stripping and smoothing are performed to isolate the brain region.
2. Histogram thresholding is used to detect if the brain is normal or abnormal by comparing histograms of halves of the brain image.
3. For abnormal brains, a modified gradient vector flow (GVF) model is used to create a force field and detect the tumor contour.
4. The area of the tumor is then calculated. The method aims to minimize segmentation time by skipping segmentation for normal brains detected in Step 2. Validation with expert segmentation is performed.
Mri brain tumour detection by histogram and segmentationiaemedu
This document summarizes a research paper on detecting brain tumors in MRI images using a combination of histogram thresholding, modified gradient vector field (GVF), and morphological operators. The non-brain regions are removed using morphological operators. Histogram thresholding is then used to detect if the brain is normal or abnormal/contains a tumor. If abnormal, the modified GVF is used to detect the tumor contour. The proposed method aims to be computationally efficient by only performing segmentation if a tumor is detected. It was tested on many MRI brain images and performance was validated against human expert segmentation.
Mri brain tumour detection by histogram and segmentation by modified gvf model 2IAEME Publication
The document describes a new method for detecting and segmenting brain tumors in MRI images. It combines histogram thresholding, modified gradient vector flow, and morphological operators. Histogram thresholding is used to detect if the brain is normal or abnormal. If abnormal, modified GVF is used to segment the tumor contour. Otherwise, segmentation is skipped to minimize computation time. The method was tested on many MRI brain images and tumor detection and dimensions were validated against expert segmentation. It provides an efficient and computationally inexpensive approach for brain tumor detection and segmentation in MRI images.
Comparison of resting electroencephalogram coherence in patients with mild co...IJECEIAES
Mild cognitive impairment (MCI) was a condition beginning before more serious deterioration, leading to Alzheimer’s dementia (AD). MCI detection was needed to determine the patient's therapeutic management. Analysis of electroencephalogram (EEG) coherence is one of the modalities for MCI detection. Therefore, this study investigated the inter and intra-hemispheric coherence over 16 EEG channels in the frequency range of 1-30 Hz. The simulation results showed that most of the electrode pair coherence in MCI patients have decreased compared to normal elderly subjects. In inter hemisphere coherence, significant differences (p<0.05) were found in the FP1-FP2 electrode pairs. Meanwhile, significant differences (p<0.05) were found in almost all pre-frontal area connectivity of the intra-hemisphere coherence pairs. The electrode pairs were FP2-F4, FP2-T4, FP1-F3, FP1-F7, FP1-C3, FP1-T3, FP1-P3, FP1-T5, FP1-O1, F3-O1, and T3-T5. The decreased coherence in MCI patients showed the disconnection of cortical connections as a result of the death of the neurons. Furthermore, the coherence value can be used as a multimodal feature in normal elderly subjects and MCI. It is hoped that current studies may be considered for early detection of Alzheimer’s in a larger population.Mild cognitive impairment (MCI) was a condition beginning before more serious deterioration, leading to Alzheimer’s dementia (AD). MCI detection was needed to determine the patient's therapeutic management. Analysis of electroencephalogram (EEG) coherence is one of the modalities for MCI detection. Therefore, this study investigated the inter and intra-hemispheric coherence over 16 EEG channels in the frequency range of 1-30 Hz. The simulation results showed that most of the electrode pair coherence in MCI patients have decreased compared to normal elderly subjects. In inter hemisphere coherence, significant differences (p<0.05) were found in the FP1-FP2 electrode pairs. Meanwhile, significant differences (p<0.05) were found in almost all pre-frontal area connectivity of the intra-hemisphere coherence pairs. The electrode pairs were FP2-F4, FP2-T4, FP1-F3, FP1-F7, FP1-C3, FP1-T3, FP1-P3, FP1-T5, FP1-O1, F3-O1, and T3-T5. The decreased coherence in MCI patients showed the disconnection of cortical connections as a result of the death of the neurons. Furthermore, the coherence value can be used as a multimodal feature in normal elderly subjects and MCI. It is hoped that current studies may be considered for early detection of Alzheimer’s in a larger population.
The biomedical profession has gained importance due to the rapid and accurate diagnosis of clinical patients using computer-aided diagnosis (CAD) tools.
The diagnosis and treatment of Alzheimer’s disease (AD) using complementary multimodalities can improve the quality of life and mental state of patients.
In this study, we integrated a lightweight custom convolutional neural network
(CNN) model and nature-inspired optimization techniques to enhance the performance, robustness, and stability of progress detection in AD. A multi-modal
fusion database approach was implemented, including positron emission tomography (PET) and magnetic resonance imaging (MRI) datasets, to create a fused
database. We compared the performance of custom and pre-trained deep learning models with and without optimization and found that employing natureinspired algorithms like the particle swarm optimization algorithm (PSO) algorithm significantly improved system performance. The proposed methodology,
which includes a fused multimodality database and optimization strategy, improved performance metrics such as training, validation, test accuracy, precision, and recall. Furthermore, PSO was found to improve the performance of
pre-trained models by 3-5% and custom models by up to 22%. Combining different medical imaging modalities improved the overall model performance by
2-5%. In conclusion, a customized lightweight CNN model and nature-inspired
optimization techniques can significantly enhance progress detection, leading to
better biomedical research and patient care.
Classification of Abnormalities in Brain MRI Images Using PCA and SVMIJERA Editor
The impact of digital image processing is increasing by the day for its use in the medical and research areas. Medical image classification scheme has been on the increase in order to help physicians and medical practitioners in their evaluation and analysis of diseases. Several classification schemes such as Artificial Neural Network (ANN), Bayes Classification, Support Vector Machine (SVM) and K-Means Nearest Neighbor have been used. In this paper, we evaluate and compared the performance of SVM and PCA by analyzing diseased image of the brain (Alzheimer) and normal (MRI) brain. The results show that Principal Components Analysis outperforms the Support Vector Machine in terms of training time and recognition time.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...IJEECSIAES
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...nooriasukmaningtyas
The document describes a proposed model for MRI brain diagnosis using genetic algorithms, convolutional neural networks, and the Internet of Things. The model has eight steps: loading MRI images, enhancing images, applying discrete wavelet transform, evaluating images using entropy, applying genetic algorithm for registration, subtracting images and using CNN to classify results as normal or abnormal, and sending messages to patients using Arduino and GSM. The model was tested on 550 normal and 350 abnormal MRI images, achieving 98.8% accuracy in classification.
MULTIPLE SCLEROSIS DIAGNOSIS WITH FUZZY C-MEANScscpconf
Magnetic resonance imaging (MRI) can support and substitute clinical information in the
diagnosis of multiple sclerosis (MS) by presenting lesion. In this paper, we present an algorithm
for MS lesion segmentation. We revisit the modification of properties of fuzzy c means
algorithms and the canny edge detection. Using reformulated fuzzy c means algorithms, apply
canny contraction principle, and establish a relationship between MS lesions and edge
detection. For the special case of FCM, we derive a sufficient condition for fixed lesions,
allowing identification of them as (local) minima of the objective function.
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography I...ijcseit
The research work presented in this paper is to achieve the tissue classification and automatically
diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet
based statistical texture analysis method. Comparative studies of texture analysis method are performed
for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method
(SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii)
Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A
wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm
(GA) is used to select the optimal texture features from the set of extracted texture features. We construct
the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by
comparing the classification results of the SVM based classifier with the Back Propagation Neural network
classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis
methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results
show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system
has been tested with a number of real Computed Tomography brain images and has achieved satisfactory
results.
Efficient Brain Tumor Detection Using Wavelet TransformIJERA Editor
Brain tumor detection is a challenging task and its very important to analyze the structure of the tumor correctly so a automatic method is used now a days for the detection of the tumor. This method saves time as well as it reduces the error which occurs in the method of manual detection. In this paper the tumor is detected using wavelet transform. MRI is an important tool used in many fields of medicine and is capable of generating a detailed image of any part of the human body. The tumor is segmented from the MRI images, features are extracted and then the area of the tumor is determined. PNN can successfully handle the process of brain tumor classification
Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Proce...IJECEIAES
The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy.
IRJET - Fusion of CT and MRI for the Detection of Brain Tumor by SWT and Prob...IRJET Journal
This document summarizes a research paper that proposes a method for detecting brain tumors by fusing CT and MRI images using stationary wavelet transform and a probabilistic neural network classifier. The proposed method involves preprocessing the CT and MRI images using median filtering for noise removal. It then applies stationary wavelet transform to the images to extract features before segmenting the tumor region using k-means clustering. Finally, the probabilistic neural network classifier determines if the tumor is benign or malignant based on the fused image features. The paper reviews other existing fusion and classification methods and argues that the proposed stationary wavelet transform and probabilistic neural network approach provides better detection of brain tumors.
International Refereed Journal of Engineering and Science (IRJES) is a peer reviewed online journal for professionals and researchers in the field of computer science. The main aim is to resolve emerging and outstanding problems revealed by recent social and technological change. IJRES provides the platform for the researchers to present and evaluate their work from both theoretical and technical aspects and to share their views.
A modified residual network for detection and classification of Alzheimer’s ...IJECEIAES
Alzheimer's disease (AD) is a brain disease that significantly declines a person's ability to remember and behave normally. By applying several approaches to distinguish between various stages of AD, neuroimaging data has been used to extract different patterns associated with various phases of AD. However, because the brain patterns of older adults and those in different phases are similar, researchers have had difficulty classifying them. In this paper, the 50-layer residual neural network (ResNet) is modified by adding extra convolution layers to make the extracted features more diverse. Besides, the activation function (ReLU) was replaced with (Leaky ReLU) because ReLU takes the negative parts of its input, drops them to zero, and retains the positive parts. These negative inputs may contain useful feature information that could aid in the development of high-level discriminative features. Thus, Leaky ReLU was used instead of ReLU to prevent any potential loss of input information. In order to train the network from scratch without encountering the issue of overfitting, we added a dropout layer before the fully connected layer. The proposed method successfully classified the four stages of AD with an accuracy of 97.49 % and 98 % for precision, recall, and f1-score.
Evaluation of Default Mode Network In Mild Cognitive Impairment and Alzheimer...CSCJournals
Although progressive functional brain network disorders has been one of the indication of Alzheimer's disease, The current research on aging and dementia focus on diagnostics of the cognitive changes of normal aging and Alzheimer Disease (AD), these changes known as Mild Cognitive Impairment (MCI). The default mode network (DMN) is a network of interacting brain regions known to have activity highly correlated with each other and distinct from other networks in the brain, the default mode network is active during passive rest and consists of a set of brain areas that are tightly functionally connected and distinct from other systems within the brain. Anatomically, the DMN includes the posterior cingulated cortex (PCC), dorsal and ventral medial prefrontal cortex, the lateral parietal cortex, and the medial temporal lobes. DMN involves multiple anatomical networks that converge on cortical hubs, such as the PCC, ventral medial prefrontal, and inferior parietal cortices. The aim of this study was to evaluate the default mode network functional connectivity in MCI patients. While no treatments are recommended for MCI currently, Mild Cognitive Impairment is becoming a very important subject for researchers and deserves more recognition and further study, In order to increase the ability to recognize earlier symptoms of Alzheimer's disease.
Similar to Alzheimer’s detection through neuro imaging and subsequent fusion for clinical diagnosis (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
- Allow user to assume the role.
- Perform administrative actions.
- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Software Engineering and Project Management - Software Testing + Agile Method...Prakhyath Rai
Software Testing: A Strategic Approach to Software Testing, Strategic Issues, Test Strategies for Conventional Software, Test Strategies for Object -Oriented Software, Validation Testing, System Testing, The Art of Debugging.
Agile Methodology: Before Agile – Waterfall, Agile Development.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
Mechatronics is a multidisciplinary field that refers to the skill sets needed in the contemporary, advanced automated manufacturing industry. At the intersection of mechanics, electronics, and computing, mechatronics specialists create simpler, smarter systems. Mechatronics is an essential foundation for the expected growth in automation and manufacturing.
Mechatronics deals with robotics, control systems, and electro-mechanical systems.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Home security is of paramount importance in today's world, where we rely more on technology, home
security is crucial. Using technology to make homes safer and easier to control from anywhere is
important. Home security is important for the occupant’s safety. In this paper, we came up with a low cost,
AI based model home security system. The system has a user-friendly interface, allowing users to start
model training and face detection with simple keyboard commands. Our goal is to introduce an innovative
home security system using facial recognition technology. Unlike traditional systems, this system trains
and saves images of friends and family members. The system scans this folder to recognize familiar faces
and provides real-time monitoring. If an unfamiliar face is detected, it promptly sends an email alert,
ensuring a proactive response to potential security threats.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Digital Twins Computer Networking Paper Presentation.pptx
Alzheimer’s detection through neuro imaging and subsequent fusion for clinical diagnosis
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 2, April 2023, pp. 1491~1498
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i2.pp1491-1498 1491
Journal homepage: http://ijece.iaescore.com
Alzheimer’s detection through neuro imaging and subsequent
fusion for clinical diagnosis
Bhavana Valsala1
, Krishnappa Honnamachanahalli Kariputtaiah2
1
Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham,
Bengaluru, India
2
Department of Computer Science and Engineering, R V College of Engineering, Bengaluru, India
Article Info ABSTRACT
Article history:
Received Feb 18, 2022
Revised Sep 1, 2022
Accepted Oct 11, 2022
In recent years, vast improvement has been observed in the field of medical
research. Alzheimer's is the most common cause for dementia. Alzheimer's
disease (AD) is a chronic disease with no cure, and it continues to pose a
threat to millions of lives worldwide. The main purpose of this study is to
detect the presence of AD from magnetic resonance imaging (MRI) scans
through neuro imaging and to perform fusion process of both MRI and
positron emission tomography (PET) scans of the same patient to obtain a
fused image with more detailed information. Detection of AD is done by
calculating the gray matter and white matter volumes of the brain and
subsequently, a ratio of calculated volume is taken which helps the doctors
in deciding whether the patient is affected with or without the disease. Image
fusion is carried out after preliminary detection of AD for MRI scan along
with PET scan. The main objective is to combine these two images into a
single image which contains all the possible information together. The
proposed approach yields better results with a peak signal to noise ratio of
60.6 dB, mean square error of 0.0176, entropy of 4.6 and structural
similarity index of 0.8.
Keywords:
Alzheimer's disease
Brain volume calculation
Discrete wavelet transform
Image fusion
Performance metrics
Pre-processing
This is an open access article under the CC BY-SA license.
Corresponding Author:
Bhavana Valsala
Department of Electronics and Communication Engineering, Amrita School of Engineering
Bengaluru, Amrita Vishwa Vidyapeetham, India
Email: bhavanapyarilal@gmail.com
1. INTRODUCTION
Dementia is one of the primary health concerns among the elderly/ageing population in the society.
Dementia is a more common term for memory loss and the loss of other cognitive abilities that directly and
heavily affect day today life of the suffering person. Alzheimer's disease (AD) is the 7th
most common reason
for the death of the people all around the world and the most common cause of dementia. In India alone,
more than 4 million people suffer from one or the other form of dementia. The first noticeable change of AD
is the difficulty in recalling events of a near past, lack of self-care, behavioral changes, and there is no cure
for this. The pre-dementia stage symptoms are very similar to that of natural ageing, and hence it is difficult
to notice and tell apart these as clear indicators of the disease. According to statistics, about 60-80 percent of
all dementia cases evolve into Alzheimer’s and detection of AD in its later stages makes it even more tough
for the patient and their family to cope with it [1].
There are various imaging modalities that can be used to detect the early biomarkers of Alzheimer’s;
the typically used techniques are: single photon emission computed tomography (SPECT), positron emission
tomography (PET), and magnetic resonance imaging (MRI) [2]. MRI displays tissue information but does not
give structural information; this is available in CT scans. PET scans give information regarding the flow of
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Int J Elec & Comp Eng, Vol. 13, No. 2, April 2023: 1491-1498
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blood in the area scanned. Research has been done in this area with respect to fusion of images for a final
scan that yields more information in comparison with the result of individual imaging modalities. The
imaging techniques named are interactive and can be used to highlight specific and different parts of the
scanned area [3]. However, individually, they have their own drawbacks.
Several academic reports based on the detection of Alzheimer's using neuroimaging, computer aided
diagnostic systems (CADS) and medical image fusion were studied to gain an understanding about the current
scenario and the new technologies explored in this research. Anitha et al. [4] described the use of image
segmentation to detect the disease. Wavelet transform, is used to denoise the image after which a 3-D image
is generated; the middle slice of which, is segmented to differentiate white matter (WM) from gray matter
(GM) area present and their ratio is calculated leading to a conclusive decision regarding the presence of AD.
Biju et al. [5] defined specific ratios obtained by processing MRI scans pertaining to which the state
of the brain can be classified into normal, 1st
stage Alzheimer's, or 2nd
stage Alzheimer's. It follows a method
of calculation of WM and GM present in three cross sections of the brain visualized through MR images [6],
namely, axial, coronal and sagittal cross sections. An average of the three white and black pixel counts
calculated are used to classify the brain scan under different stages of the disease as aforementioned. Taie and
Ghonaim [7] described a new model for the early diagnosis of AD based on bat-support vector machine
(SVM) classifier. Firstly, features of MRI brain images to build feature vector of brain are obtained. Then the
most significant features are extracted using neuroimaging to reduce high dimensional space of MR images
and is further classified using machine learning techniques. This bat-SVM model achieved an acceptable
accuracy of about 95.36%.
Supriyanti et al. [8] has seen a good improvement in analyzing early AD detection with the help of
watershed method particularly on coronal slice analysis. The process of identifying images with AD and
healthy models are done based on the morphological analysis, by referring to the values of clinical dementia
rating (CDR), that has given acceptable results. In the process of fusion, after pre-processing techniques [9]–
[13] is applied on an image, it decomposes the images into two components, i.e., approximate coefficients
and detailed coefficients. The decomposition happens in both the images and thus, two sets of approximate
and detailed coefficients are obtained [14], [15]. These extracted set of approximate coefficients are fused
together, and the set of detailed coefficients are fused together and to which fusion rules are applied. To
check whether the process holds good or not, the reconstruction process is performed to determine the loss in
the mutual information. This involves the use of Haar transform. Image fusion improves the accuracy of
detection and the probability of detecting small changes is very high with this process [16], [17].
Multimodal medical image fusion [18] has seen a rise in medical imaging because single modality
images lack the ability to provide all the information required in accurately diagnosing a disease. Image
fusion techniques proposed includes incorporation of benefits of cross correlation and even degree coefficient
fusion along with discrete wavelet transform (DWT) to enhance fused output [19]. The source images are
first pre-processed by splitting color channels of PET image, decomposing it into its intensity, hue, and
saturation (HIS) transforms and performing DWT [20]–[24] that yields coefficients which are then fused, and
inverse transformed back to original hue and saturation coordinates yielding final outputs [25], [26]. This
work was evaluated using performance metrics studied in existing literatures namely peak signal to noise
ratio (PSNR) and root mean square error (RMSE) [27]. PET image is decomposed into its IHS transform and
using this proposed IHS substitution process, fused image is identified to have enhanced anatomical and
clinical data. The high frequency coefficient of MRI and PET images is combined with the transformation
later using the averaging technique. Inverse DWT is performed to get the resultant fused output [28]–[31].
A modern multi-modal image fusion approach was used in this study [32] to incorporate the benefits
of cross correlation and even degree approaches into image fusion techniques. Firstly, two source images are
read. PET images are decomposed into its respective color channels and DWT. Even degree method is used
to fuse the low and high frequency coefficients. To obtain a fused intensity image, inverse DWT is applied to
the fused coefficients. Finally, new intensity image coordinate is interpreted into red, green, blue (RGB)
coordinates with its original hue and saturation coordinates to generate a fused image. Diagnosis of
Alzheimer's at an early stage provides a better chance of benefiting the individual from treatment. These
include heart disease, diabetes, stroke, high blood pressure, high cholesterol, even obesity which aims at
proposing a computer aided diagnosis system (CADS) for the detection and diagnosis of AD using
neuroimaging followed by fusion of AD detected MRI with PET scans which results in an image that
contains the structural information and anatomical information, of both scans respectively, enabling
physicians to provide a more informed diagnosis.
The rest of the paper is organized as follows. Section 2 details the proposed approaches of
Alzheimer’s detection and fusion implementation and explains the steps involved and its architecture. Section
3 explains the results and analysis of the model and compares their performance metrics. Section 4 concludes
the work implemented in this paper and describes the future scope.
3. Int J Elec & Comp Eng ISSN: 2088-8708
Alzheimer’s detection through neuro imaging and subsequent fusion for clinical diagnosis (Bhavana Valsala)
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2. PROPOSED METHOD
The input dataset consists of axial, coronal, and sagittal T1 weighted 256×256 sized MRI brain scans.
These datasets are obtained from the Harvard Brain Atlas website. The implementation of this work is
conducted using a subset of 6 brain scans, three healthy brain scans and three abnormal brain scans. The
implementation of this work is developed and assessed using MATLAB and OpenCV. The abnormal brain
scans are those of Alzheimer infected brains. Figure 1 represents the block diagram depicting the method of
detection of AD. Method to detect AD consists of image pre-processing as its first step. The main aim is to
perform operations on the images at the most basic level to enhance image features and suppress any undesired
distortions and noise components without changing the image information content. The operations include
image conversion, image resizing, intensity adjustment, filtering and noise removal and histogram equalization.
Figure 1. Block diagram depicting method of detection of AD
Filtering, as for one-dimensional signals, can also be used to process images i.e., images can be
passed through low-pass filters (LPF) and high-pass filters (HPF). LPF helps to remove noise by making
images blurred. High pass filters help to find edges in images. The transfer function for the ideal low pass
filter can be given as:
𝐻(𝑥, 𝑦) = {
1 𝑖𝑓 𝐷(𝑥, 𝑦) < 𝐷𝑜
0 𝑖𝑓 𝐷(𝑥, 𝑦) > 𝐷𝑜
(1)
where Do is a positive constant, all frequencies within the circle of radius Do is passed by the filter, which is
termed as the cut-off frequency. D(x,y) is the Euclidean distance from any point on the frequency plane to the
4. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 2, April 2023: 1491-1498
1494
origin. A high pass filter is used to sharpen images, i.e., it passes all frequencies above the cut-off value Do,
having transfer function.
𝐻 (𝑥, 𝑦) = {
0 𝑖𝑓 𝐷(𝑥, 𝑦) < 𝐷𝑜
1 𝑖𝑓 𝐷(𝑥, 𝑦) > 𝐷𝑜
(2)
Histogram equalization is a technique of image processing that is used to enhance the contrast in
images. This is accomplished by evenly distributing the intensity values of the most frequent occurrences.
Input pixel intensity, x is transformed to a new value x' by T. T is the transform function that is the product of
a cumulative histogram and a scale factor:
x’ = T(x) = ∑ 𝑛𝑖
𝑥
𝑖=1
𝑚𝑎𝑥.𝑖𝑛𝑡𝑒𝑛𝑠𝑖𝑡𝑦
𝑁
(3)
where number of pixels at intensity i is depicted by 𝑛𝑖 and the total number of pixels in the image by N. This
process allows areas of lower local contrast to achieve a higher contrast
Image segmentation is then performed to differentiate WM from GM followed by skull stripping to
remove the skull from the scan and avoid calculation of the skull portion as extra white pixels. Otsu’s
thresholding algorithm is used to implement clustering automatically by reducing gray image to a binary
image which proves to be the most efficient for required threshold calculation. The algorithm iteratively
searches for a threshold that minimizes the within-class variance of the background (<threshold) and
foreground (>threshold). Class variance is defined as:
σ2(𝑡) = 𝜔𝑏𝑔(𝑡)σ2
𝑏𝑔(𝑡) + 𝜔𝑓𝑔(𝑡)σ2
𝑓𝑔(𝑡) (4)
where 𝜔𝑏𝑔(𝑡) and 𝜔𝑓𝑔(𝑡) represents the probability of number of pixels for each class at threshold t and 𝜎2
represents variance of color values.
The skull stripping method behaves as a preliminary step in various medical applications. It boosts
precision of diagnosis and gets rid of non-cerebral tissues like skull, scalp, and dura from brain images.
Thereby, an algorithm of skull stripping produces a mask that is overlapped onto the original image and the
largest connected component is extracted, i.e., the brain. This is followed by masking to remove the unnecessary
background portion of skull stripped image. Then the amount of GM and WM are calculated for all three
cross-sections and the resultant volume of GM and WM is obtained. The overall brain volume is obtained by
adding the volume of all the planes. Experimental findings show that if the ratio is more than 0.6, then the
person is normal; if the ratio is less than 0.6, then the person is AD affected.
Image fusion of PET and MRI, at its onset, consists of image denoising using a Gaussian filter and
smoothening the entire image. For processing and fusion of gray scaled image with colored image, it is
necessary that the images are of the same color scheme. Hence, the RGB scheme PET images are separated
into individual red, green and blue channels. Figure 2 represents the process of DWT. Discrete wavelet
transform (DWT) is applied to achieve four coefficients, which are LL, LH, HL, and HH coefficients, also
known as the approximate and detailed coefficients representing the horizontal and vertical directions of
image, respectively.
Figure 2. Process of DWT of 2-D image
Figure 3 depicts the block diagram for image fusion. On the individually acquired coefficients for
three separate color channels, different fusion rules are applied. Approximation coefficients (LL) of both
5. Int J Elec & Comp Eng ISSN: 2088-8708
Alzheimer’s detection through neuro imaging and subsequent fusion for clinical diagnosis (Bhavana Valsala)
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MRI and PET scans are fused using one rule and detailed coefficients (LH, HL, HH) are fused using another
rule. For e.g., in the case of max-mean rule of fusion, approximation coefficient of PET and MRI are fused
using maximum selection rule and the rest of the coefficients are fused using mean selection rule. Nine fusion
rules were explored and implemented and finally, it was concluded from the visual outputs yielded that
mean-mean fusion rule is more appropriate. Lastly, inverse DWT is applied to fuse all the coefficients back
into one image and further fused coefficients of the three different color channels are merged back to form
final fused image that contains information of both the MRI and PET images. Final image is evaluated using
performance criteria and validated.
Figure 3. Block diagram depicting method of image fusion
3. RESULTS AND DISCUSSION
The outputs, and ratios achieved from the MRI scans, yielded from this work have been compared
with reference values and is proven to be good.
a. For Axial cross section:
- Number of white pixels: WM: 391861
- Number of black pixels: GM: 227314
b. For Coronal cross section:
- Number of white pixels: WM: 439155
- Number of black pixels: GM: 179661
c. For Sagittal cross section:
- Number of white pixels: WM: 437641
- Number of black pixels: GM: 178552
d. Final ratio: 0.46
Similarly, final ratio for the second dataset used is found to be 0.77. These values are in accordance
with the reference values retrieved from related literature survey and from the prior knowledge that the first
dataset is from an Alzheimer's patient and the latter of a normal brain is concluded to be accurate.
6. ISSN: 2088-8708
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Further, fusion results have been evaluated with:
a. Entropy: This is an important index to measure the degree of information rich in images. A greater value of
entropy indicates that there is higher information content in fused image and is of better quality,
𝑒𝑛𝑡𝑟𝑜𝑝𝑦 =
1
𝑘
(∑ (𝑝𝑘 (𝑧))𝑙𝑜𝑔2(𝑝𝑘(𝑧))
𝑖
255
𝑖=0 ) (5)
where k=R, G, B, and p(z} denoting the probability of ith
intensity.
b. Mean square error (MSE): This metric is for measuring image compression quality. MSE represents the
cumulative squared error when the fused image is in comparison with the original one. Lower value of
MSE indicates lower error.
𝑀𝑆𝐸 =
1
𝑀𝑁
∑ ∑ [(𝐼(𝑥, 𝑦) − 𝐼′(𝑥, 𝑦)]2
𝑁
𝑋=1
𝑀
𝑦=1 (6)
c. PSNR: It is used to figure out the degree of image misrepresentation and is calculated as the ratio of
power of the signal to the level of power of the noise. The higher the PSNR value, the better the fusion.
𝑃𝑆𝑁𝑅 = 10 ∗ 𝑙𝑜𝑔10
(255∗255)
𝑀𝑆𝐸
(7)
d. Structural similarity (SSIM) index: It measures quality of fused image with respect to a reference image.
It quantifies image degradation cause by processing. Maximum value is +1 and it indicates that the fused
and reference image are very similar or same.
𝑆𝑆𝐼𝑀(𝑥, 𝑦) = [𝑙(𝑥, 𝑦)𝛼 ][𝑐(𝑥, 𝑦)𝛽
][𝑠(𝑥, 𝑦)𝛾] (8)
The outputs obtained for 3 datasets are depicted in Figures 4 to 6. Performance metrics like entropy,
PSNR, MSE, SSIM are calculated for the fusion process using mean-mean fusion rule and has been tabulated
as shown in Table 1. The proposed approach yielded good results with performance metric values of PSNR
of 60.6 dB, MSE value as low as 0.0176, entropy of 4.6 and SSIM index of 0.8. This methodology helps the
medical experts in a larger scale for better clinical diagnosis.
Figure 4. Input PET (left) fused with input MRI (middle) to give final fused output (right) in dataset 1
Figure 5. Input PET (left) fused with input MRI (middle) to give final fused output (right) in dataset 2
Figure 6. Input PET (left) fused with input MRI (middle) to give final fused output (right) in dataset 3
7. Int J Elec & Comp Eng ISSN: 2088-8708
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Table 1. Performance metrics of final outputs
Performance metrics Dataset 1 Dataset 2 Dataset 3
Entropy of fused image (bits/pixel) 4.6139 3.9779 4.4062
PSNR (dB) 59.6135 60.6098 59.5595
MSE 0.0176 0.0570 0.0725
SSIM 0.7587 0.8083 0.7614
4. CONCLUSION
In this work, we have proposed detection of AD using two different types of brain scans.
A comparative study was conducted using T-1 weighted MRI scans of brains, with and without AD.
The automated process has very low time complexity. The results overcome the problem of existence of skull
portion with respect to other existing methodologies. Detection of AD from scans based on the calculation of
area of white and black pixels in the resultant grayscale image and thereby, ratio of the volume of white
matter to gray matter output obtained from the algorithm’s implementation are found to be in accordance
with the reference values collected. Results yielded shows good accuracy with respect to the comparative
study of a healthy brain vs not. Image fusion was carried out after preliminary detection of AD for MRI
along with PET scans. Pre-processing was carried out for both the scans and 2-level DWT based fusion
was implemented. Performance metrics like entropy, PSNR, MSE and SSIM index were used to validate
the system which has given promising results. This work can also be extended to use with other imaging
modalities such as SPECT, and CT. Two-level DWT has been implemented but higher levels of DWT using
other wavelets and fusion of DWT with other methods such as PCA. can be explored to make the fusion
outcomes better.
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BIOGRAPHIES OF AUTHORS
Bhavana Valsala is working as assistant professor (Sr. Gr.) in the Department of
Electronics and Communication Engineering, Amrita Vishwa Vidyapeetham, Amrita School of
Engineering, Bengaluru. She has a teaching experience of 13 years in the Department of
Electronics and Communication Engineering. She is currently pursuing her research in the field
of medical image processing. She has published around 20 research articles in reputed journals
and conferences. She can be contacted at email: bhavanapyarilal@gmail.com.
Krishnappa Honnamachanahalli Kariputtaiah is working as Associate
Professor in the Department of Computer Science and Engineering, R V College of
Engineering, Bengaluru. He has a teaching experience of 22 years in the Department of
Computer Science and Engineering. He has received his Ph.D. from Visvesvaraya
Technological University in the field of graph theory. He has published around 22 research
articles in reputed journals and conferences. He can be contacted at krishnappahk@rvce.edu.in.