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.
Overview of convolutional neural networks architectures for brain tumor segm...IJECEIAES
Due to the paramount importance of the medical field in the lives of people, researchers and experts exploited advancements in computer techniques to solve many diagnostic and analytical medical problems. Brain tumor diagnosis is one of the most important computational problems that has been studied and focused on. The brain tumor is determined by segmentation of brain images using many techniques based on magnetic resonance imaging (MRI). Brain tumor segmentation methods have been developed since a long time and are still evolving, but the current trend is to use deep convolutional neural networks (CNNs) due to its many breakthroughs and unprecedented results that have been achieved in various applications and their capacity to learn a hierarchy of progressively complicated characteristics from input without requiring manual feature extraction. Considering these unprecedented results, we present this paper as a brief review for main CNNs architecture types used in brain tumor segmentation. Specifically, we focus on researcher works that used the well-known brain tumor segmentation (BraTS) dataset.
DiaMe: IoMT deep predictive model based on threshold aware region growing tec...IJECEIAES
Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.
Overview of convolutional neural networks architectures for brain tumor segm...IJECEIAES
Due to the paramount importance of the medical field in the lives of people, researchers and experts exploited advancements in computer techniques to solve many diagnostic and analytical medical problems. Brain tumor diagnosis is one of the most important computational problems that has been studied and focused on. The brain tumor is determined by segmentation of brain images using many techniques based on magnetic resonance imaging (MRI). Brain tumor segmentation methods have been developed since a long time and are still evolving, but the current trend is to use deep convolutional neural networks (CNNs) due to its many breakthroughs and unprecedented results that have been achieved in various applications and their capacity to learn a hierarchy of progressively complicated characteristics from input without requiring manual feature extraction. Considering these unprecedented results, we present this paper as a brief review for main CNNs architecture types used in brain tumor segmentation. Specifically, we focus on researcher works that used the well-known brain tumor segmentation (BraTS) dataset.
DiaMe: IoMT deep predictive model based on threshold aware region growing tec...IJECEIAES
Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.
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.
Medical image is an important parameter for diagnosis to many diseases. Now day’s
telemedicine is major treatment based on medical images. The World Health Organization
(WHO) established the Global Observatory for eHealth (GOe) to review the benefits that
Information and communication technologies (ICTs) can bring to health care and patients’
wellbeing. Securing medical images is important to protect the privacy of patients and assure
data integrity. In this paper a new self-adaptive medical image encryption algorithm is proposed
to improve its robustness. A corresponding size of matrix in the top right corner was created by
the pixel gray-scale value of the top left corner under Chebyshev mapping. The gray-scale value
of the top right corner block was then replaced by the matrix created before. The remaining
blocks were encrypted in the same manner in clockwise until the top left corner block was finally
encrypted. This algorithm is not restricted to the size of image and it is suitable to gray images
and color images, which leads to better robustness. Meanwhile, the introduction of gray-scale value diffusion system equips this algorithm with powerful function of diffusion and disturbance.
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
An architectural framework for automatic detection of autism using deep conv...IJECEIAES
The brainchild in any medical image processing lied in how accurately the diseases are diagnosed. Especially in the case of neural disorders such as autism spectrum disorder (ASD), accurate detection was still a challenge. Several noninvasive neuroimaging techniques provided experts information about the functionality and anatomical structure of the brain. As autism is a neural disorder, magnetic resonance imaging (MRI) of the brain gave a complex structure and functionality. Many machine learning techniques were proposed to improve the classification and detection accuracy of autism in MRI images. Our work focused mainly on developing the architecture of convolution neural networks (CNN) combining the genetic algorithm. Such artificial intelligence (AI) techniques were very much needed for training as they gave better accuracy compared to traditional statistical methods.
Brain tumor classification in magnetic resonance imaging images using convol...IJECEIAES
Deep learning (DL) is a subfield of artificial intelligence (AI) used in several sectors, such as cybersecurity, finance, marketing, automated vehicles, and medicine. Due to the advancement of computer performance, DL has become very successful. In recent years, it has processed large amounts of data, and achieved good results, especially in image analysis such as segmentation and classification. Manual evaluation of tumors, based on medical images, requires expensive human labor and can easily lead to misdiagnosis of tumors. Researchers are interested in using DL algorithms for automatic tumor diagnosis. convolutional neural network (CNN) is one such algorithm. It is suitable for medical image classification tasks. In this paper, we will focus on the development of four sequential CNN models to classify brain tumors in magnetic resonance imaging (MRI) images. We followed two steps, the first being data preprocessing and the second being automatic classification of preprocessed images using CNN. The experiments were conducted on a dataset of 3,000 MRI images, divided into two classes: tumor and normal. We obtained a good accuracy of 98,27%, which outperforms other existing models.
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.
Classification of pathologies on digital chest radiographs using machine lear...IJECEIAES
This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using preprocessed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
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.
A review on detecting brain tumors using deep learning and magnetic resonanc...IJECEIAES
Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained.
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.
A deep learning approach for brain tumor detection using magnetic resonance ...IJECEIAES
The growth of abnormal cells in the brain’s tissue causes brain tumors. Brain tumors are considered one of the most dangerous disorders in children and adults. It develops quickly, and the patient’s survival prospects are slim if not appropriately treated. Proper treatment planning and precise diagnoses are essential to improving a patient’s life expectancy. Brain tumors are mainly diagnosed using magnetic resonance imaging (MRI). As part of a convolution neural network (CNN)-based illustration, an architecture containing five convolution layers, five max-pooling layers, a Flatten layer, and two dense layers has been proposed for detecting brain tumors from MRI images. The proposed model includes an automatic feature extractor, modified hidden layer architecture, and activation function. Several test cases were performed, and the proposed model achieved 98.6% accuracy and 97.8% precision score with a low cross-entropy rate. Compared with other approaches such as adjacent feature propagation network (AFPNet), mask region-based CNN (mask RCNN), YOLOv5, and Fourier CNN (FCNN), the proposed model has performed better in detecting brain tumors.
Development of depth map from stereo images using sum of absolute differences...nooriasukmaningtyas
This article proposes a framework for the depth map reconstruction using stereo images. Fundamentally, this map provides an important information which commonly used in essential applications such as autonomous vehicle navigation, drone’s navigation and 3D surface reconstruction. To develop an accurate depth map, the framework must be robust against the challenging regions of low texture, plain color and repetitive pattern on the input stereo image. The development of this map requires several stages which starts with matching cost calculation, cost aggregation, optimization and refinement stage. Hence, this work develops a framework with sum of absolute difference (SAD) and the combination of two edge preserving filters to increase the robustness against the challenging regions. The SAD convolves using block matching technique to increase the efficiency of matching process on the low texture and plain color regions. Moreover, two edge preserving filters will increase the accuracy on the repetitive pattern region. The results show that the proposed method is accurate and capable to work with the challenging regions. The results are provided by the Middlebury standard dataset. The framework is also efficiently and can be applied on the 3D surface reconstruction. Moreover, this work is greatly competitive with previously available methods.
Model predictive controller for a retrofitted heat exchanger temperature cont...nooriasukmaningtyas
This paper aims to demonstrate the practical aspects of process control theory for undergraduate students at the Department of Chemical Engineering at the University of Bahrain. Both, the ubiquitous proportional integral derivative (PID) as well as model predictive control (MPC) and their auxiliaries were designed and implemented in a real-time framework. The latter was realized through retrofitting an existing plate-and-frame heat exchanger unit that has been operated using an analog PID temperature controller. The upgraded control system consists of a personal computer (PC), low-cost signal conditioning circuit, national instruments USB 6008 data acquisition card, and LabVIEW software. LabVIEW control design and simulation modules were used to design and implement the PID and MPC controllers. The performance of the designed controllers was evaluated while controlling the outlet temperature of the retrofitted plate-and-frame heat exchanger. The distinguished feature of the MPC controller in handling input and output constraints was perceived in real-time. From a pedagogical point of view, realizing the theory of process control through practical implementation was substantial in enhancing the student’s learning and the instructor’s teaching experience.
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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.
Medical image is an important parameter for diagnosis to many diseases. Now day’s
telemedicine is major treatment based on medical images. The World Health Organization
(WHO) established the Global Observatory for eHealth (GOe) to review the benefits that
Information and communication technologies (ICTs) can bring to health care and patients’
wellbeing. Securing medical images is important to protect the privacy of patients and assure
data integrity. In this paper a new self-adaptive medical image encryption algorithm is proposed
to improve its robustness. A corresponding size of matrix in the top right corner was created by
the pixel gray-scale value of the top left corner under Chebyshev mapping. The gray-scale value
of the top right corner block was then replaced by the matrix created before. The remaining
blocks were encrypted in the same manner in clockwise until the top left corner block was finally
encrypted. This algorithm is not restricted to the size of image and it is suitable to gray images
and color images, which leads to better robustness. Meanwhile, the introduction of gray-scale value diffusion system equips this algorithm with powerful function of diffusion and disturbance.
Glioblastomas brain tumour segmentation based on convolutional neural network...IJECEIAES
Brain tumour segmentation can improve diagnostics efficiency, rise the prediction rate and treatment planning. This will help the doctors and experts in their work. Where many types of brain tumour may be classified easily, the gliomas tumour is challenging to be segmented because of the diffusion between the tumour and the surrounding edema. Another important challenge with this type of brain tumour is that the tumour may grow anywhere in the brain with different shape and size. Brain cancer presents one of the most famous diseases over the world, which encourage the researchers to find a high-throughput system for tumour detection and classification. Several approaches have been proposed to design automatic detection and classification systems. This paper presents an integrated framework to segment the gliomas brain tumour automatically using pixel clustering for the MRI images foreground and background and classify its type based on deep learning mechanism, which is the convolutional neural network. In this work, a novel segmentation and classification system is proposed to detect the tumour cells and classify the brain image if it is healthy or not. After collecting data for healthy and non-healthy brain images, satisfactory results are found and registered using computer vision approaches. This approach can be used as a part of a bigger diagnosis system for breast tumour detection and manipulation.
The performance of artificial intelligence in prostate magnetic resonance im...IJECEIAES
Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.
An architectural framework for automatic detection of autism using deep conv...IJECEIAES
The brainchild in any medical image processing lied in how accurately the diseases are diagnosed. Especially in the case of neural disorders such as autism spectrum disorder (ASD), accurate detection was still a challenge. Several noninvasive neuroimaging techniques provided experts information about the functionality and anatomical structure of the brain. As autism is a neural disorder, magnetic resonance imaging (MRI) of the brain gave a complex structure and functionality. Many machine learning techniques were proposed to improve the classification and detection accuracy of autism in MRI images. Our work focused mainly on developing the architecture of convolution neural networks (CNN) combining the genetic algorithm. Such artificial intelligence (AI) techniques were very much needed for training as they gave better accuracy compared to traditional statistical methods.
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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.
Classification of pathologies on digital chest radiographs using machine lear...IJECEIAES
This article is devoted to the research and development of methods for classifying pathologies on digital chest radiographs using two different machine learning approaches: the eXtreme gradient boosting (XGBoost) algorithm and the deep convolutional neural network residual network (ResNet50). The goal of the study is to develop effective and accurate methods for automatically classifying various pathologies detected on chest X-rays. The study collected an extensive dataset of digital chest radiographs, including a variety of clinical cases and different classes of pathology. Developed and trained machine learning models based on the XGBoost algorithm and the ResNet50 convolutional neural network using preprocessed images. The performance and accuracy of both models were assessed on test data using quality metrics and a comparative analysis of the results was carried out. The expected results of the article are high accuracy and reliability of methods for classifying pathologies on chest radiographs, as well as an understanding of their effectiveness in the context of clinical practice. These results may have significant implications for improving the diagnosis and care of patients with chest diseases, as well as promoting the development of automated decision support systems in radiology.
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.
A review on detecting brain tumors using deep learning and magnetic resonanc...IJECEIAES
Early detection and treatment in the medical field offer a critical opportunity to survive people. However, the brain has a significant role in human life as it handles most human body activities. Accurate diagnosis of brain tumors dramatically helps speed up the patient's recovery and the cost of treatment. Magnetic resonance imaging (MRI) is a commonly used technique due to the massive progress of artificial intelligence in medicine, machine learning, and recently, deep learning has shown significant results in detecting brain tumors. This review paper is a comprehensive article suitable as a starting point for researchers to demonstrate essential aspects of using deep learning in diagnosing brain tumors. More specifically, it has been restricted to only detecting brain tumors (binary classification as normal or tumor) using MRI datasets in 2020 and 2021. In addition, the paper presents the frequently used datasets, convolutional neural network architectures (standard and designed), and transfer learning techniques. The crucial limitations of applying the deep learning approach, including a lack of datasets, overfitting, and vanishing gradient problems, are also discussed. Finally, alternative solutions for these limitations are obtained.
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.
A deep learning approach for brain tumor detection using magnetic resonance ...IJECEIAES
The growth of abnormal cells in the brain’s tissue causes brain tumors. Brain tumors are considered one of the most dangerous disorders in children and adults. It develops quickly, and the patient’s survival prospects are slim if not appropriately treated. Proper treatment planning and precise diagnoses are essential to improving a patient’s life expectancy. Brain tumors are mainly diagnosed using magnetic resonance imaging (MRI). As part of a convolution neural network (CNN)-based illustration, an architecture containing five convolution layers, five max-pooling layers, a Flatten layer, and two dense layers has been proposed for detecting brain tumors from MRI images. The proposed model includes an automatic feature extractor, modified hidden layer architecture, and activation function. Several test cases were performed, and the proposed model achieved 98.6% accuracy and 97.8% precision score with a low cross-entropy rate. Compared with other approaches such as adjacent feature propagation network (AFPNet), mask region-based CNN (mask RCNN), YOLOv5, and Fourier CNN (FCNN), the proposed model has performed better in detecting brain tumors.
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Development of depth map from stereo images using sum of absolute differences...nooriasukmaningtyas
This article proposes a framework for the depth map reconstruction using stereo images. Fundamentally, this map provides an important information which commonly used in essential applications such as autonomous vehicle navigation, drone’s navigation and 3D surface reconstruction. To develop an accurate depth map, the framework must be robust against the challenging regions of low texture, plain color and repetitive pattern on the input stereo image. The development of this map requires several stages which starts with matching cost calculation, cost aggregation, optimization and refinement stage. Hence, this work develops a framework with sum of absolute difference (SAD) and the combination of two edge preserving filters to increase the robustness against the challenging regions. The SAD convolves using block matching technique to increase the efficiency of matching process on the low texture and plain color regions. Moreover, two edge preserving filters will increase the accuracy on the repetitive pattern region. The results show that the proposed method is accurate and capable to work with the challenging regions. The results are provided by the Middlebury standard dataset. The framework is also efficiently and can be applied on the 3D surface reconstruction. Moreover, this work is greatly competitive with previously available methods.
Model predictive controller for a retrofitted heat exchanger temperature cont...nooriasukmaningtyas
This paper aims to demonstrate the practical aspects of process control theory for undergraduate students at the Department of Chemical Engineering at the University of Bahrain. Both, the ubiquitous proportional integral derivative (PID) as well as model predictive control (MPC) and their auxiliaries were designed and implemented in a real-time framework. The latter was realized through retrofitting an existing plate-and-frame heat exchanger unit that has been operated using an analog PID temperature controller. The upgraded control system consists of a personal computer (PC), low-cost signal conditioning circuit, national instruments USB 6008 data acquisition card, and LabVIEW software. LabVIEW control design and simulation modules were used to design and implement the PID and MPC controllers. The performance of the designed controllers was evaluated while controlling the outlet temperature of the retrofitted plate-and-frame heat exchanger. The distinguished feature of the MPC controller in handling input and output constraints was perceived in real-time. From a pedagogical point of view, realizing the theory of process control through practical implementation was substantial in enhancing the student’s learning and the instructor’s teaching experience.
Control of a servo-hydraulic system utilizing an extended wavelet functional ...nooriasukmaningtyas
Servo-hydraulic systems have been extensively employed in various industrial applications. However, these systems are characterized by their highly complex and nonlinear dynamics, which complicates the control design stage of such systems. In this paper, an extended wavelet functional link neural network (EWFLNN) is proposed to control the displacement response of the servo-hydraulic system. To optimize the controller's parameters, a recently developed optimization technique, which is called the modified sine cosine algorithm (M-SCA), is exploited as the training method. The proposed controller has achieved remarkable results in terms of tracking two different displacement signals and handling external disturbances. From a comparative study, the proposed EWFLNN controller has attained the best control precision compared with those of other controllers, namely, a proportional-integralderivative (PID) controller, an artificial neural network (ANN) controller, a wavelet neural network (WNN) controller, and the original wavelet functional link neural network (WFLNN) controller. Moreover, compared to the genetic algorithm (GA) and the original sine cosine algorithm (SCA), the M-SCA has shown better optimization results in finding the optimal values of the controller's parameters.
Decentralised optimal deployment of mobile underwater sensors for covering la...nooriasukmaningtyas
This paper presents the problem of sensing coverage of layers of the ocean in three dimensional underwater environments. We propose distributed control laws to drive mobile underwater sensors to optimally cover a given confined layer of the ocean. By applying this algorithm at first the mobile underwater sensors adjust their depth to the specified depth. Then, they make a triangular grid across a given area. Afterwards, they randomly move to spread across the given grid. These control laws only rely on local information also they are easily implemented and computationally effective as they use some easy consensus rules. The feature of exchanging information just among neighbouring mobile sensors keeps the information exchange minimum in the whole networks and makes this algorithm practicable option for undersea. The efficiency of the presented control laws is confirmed via mathematical proof and numerical simulations.
Evaluation quality of service for internet of things based on fuzzy logic: a ...nooriasukmaningtyas
The development of the internet of thing (IoT) technology has become a major concern in sustainability of quality of service (SQoS) in terms of efficiency, measurement, and evaluation of services, such as our smart home case study. Based on several ambiguous linguistic and standard criteria, this article deals with quality of service (QoS). We used fuzzy logic to select the most appropriate and efficient services. For this reason, we have introduced a new paradigmatic approach to assess QoS. In this regard, to measure SQoS, linguistic terms were collected for identification of ambiguous criteria. This paper collects the results of other work to compare the traditional assessment methods and techniques in IoT. It has been proven that the comparison that traditional valuation methods and techniques could not effectively deal with these metrics. Therefore, fuzzy logic is a worthy method to provide a good measure of QoS with ambiguous linguistic and criteria. The proposed model addresses with constantly being improved, all the main axes of the QoS for a smart home. The results obtained also indicate that the model with its fuzzy performance importance index (FPII) has efficiently evaluate the multiple services of SQoS.
Low power architecture of logic gates using adiabatic techniquesnooriasukmaningtyas
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Smart monitoring system using NodeMCU for maintenance of production machinesnooriasukmaningtyas
Maintenance is an activity that helps to reduce risk, increase productivity, improve quality, and minimize production costs. The necessity for maintenance actions will increase efficiency and enhance the safety and quality of products and processes. On getting these conditions, it is necessary to implement a monitoring system used to observe machines' conditions from time to time, especially the machine parts that often experience problems. This paper presents a low-cost intelligent monitoring system using NodeMCU to continuously monitor machine conditions and provide warnings in the case of machine failure. Not only does it provide alerts, but this monitoring system also generates historical data on machine conditions to the Google Cloud (Google Sheet), includes which machines were down, downtime, issues occurred, repairs made, and technician handling. The results obtained are machine operators do not need to lose a relatively long time to call the technician. Likewise, the technicians assisted in carrying out machine maintenance activities and online reports so that errors that often occur due to human error do not happen again. The system succeeded in reducing the technician-calling time and maintenance workreporting time up to 50%. The availability of online and real-time maintenance historical data will support further maintenance strategy.
Design and simulation of a software defined networkingenabled smart switch, f...nooriasukmaningtyas
Using sustainable energy is the future of our planet earth, this became not only economically efficient but also a necessity for the preservation of life on earth. Because of such necessity, smart grids became a very important issue to be researched. Many literatures discussed this topic and with the development of internet of things (IoT) and smart sensors, smart grids are developed even further. On the other hand, software defined networking is a technology that separates the control plane from the data plan of the network. It centralizes the management and the orchestration of the network tasks by using a network controller. The network controller is the heart of the SDN-enabled network, and it can control other networking devices using software defined networking (SDN) protocols such as OpenFlow. A smart switching mechanism called (SDN-smgrid-sw) for the smart grid will be modeled and controlled using SDN. We modeled the environment that interact with the sensors, for the sun and the wind elements. The Algorithm is modeled and programmed for smart efficient power sharing that is managed centrally and monitored using SDN controller. Also, all if the smart grid elements (power sources) are connected to the IP network using IoT protocols.
Efficient wireless power transmission to remote the sensor in restenosis coro...nooriasukmaningtyas
In this study, the researchers have proposed an alternative technique for designing an asymmetric 4 coil-resonance coupling module based on the series-to-parallel topology at 27 MHz industrial scientific medical (ISM) band to avoid the tissue damage, for the constant monitoring of the in-stent restenosis coronary artery. This design consisted of 2 components, i.e., the external part that included 3 planar coils that were placed outside the body and an internal helical coil (stent) that was implanted into the coronary artery in the human tissue. This technique considered the output power and the transfer efficiency of the overall system, coil geometry like the number of coils per turn, and coil size. The results indicated that this design showed an 82% efficiency in the air if the transmission distance was maintained as 20 mm, which allowed the wireless power supply system to monitor the pressure within the coronary artery when the implanted load resistance was 400 Ω.
Grid reactive voltage regulation and cost optimization for electric vehicle p...nooriasukmaningtyas
Expecting large electric vehicle (EV) usage in the future due to environmental issues, state subsidies, and incentives, the impact of EV charging on the power grid is required to be closely analyzed and studied for power quality, stability, and planning of infrastructure. When a large number of energy storage batteries are connected to the grid as a capacitive load the power factor of the power grid is inevitably reduced, causing power losses and voltage instability. In this work large-scale 18K EV charging model is implemented on IEEE 33 network. Optimization methods are described to search for the location of nodes that are affected most due to EV charging in terms of power losses and voltage instability of the network. Followed by optimized reactive power injection magnitude and time duration of reactive power at the identified nodes. It is shown that power losses are reduced and voltage stability is improved in the grid, which also complements the reduction in EV charging cost. The result will be useful for EV charging stations infrastructure planning, grid stabilization, and reducing EV charging costs.
Topology network effects for double synchronized switch harvesting circuit on...nooriasukmaningtyas
Energy extraction takes place using several different technologies, depending on the type of energy and how it is used. The objective of this paper is to study topology influence for a smart network based on piezoelectric materials using the double synchronized switch harvesting (DSSH). In this work, has been presented network topology for circuit DSSH (DSSH Standard, Independent DSSH, DSSH in parallel, mono DSSH, and DSSH in series). Using simulation-based on a structure with embedded piezoelectric system harvesters, then compare different topology of circuit DSSH for knowledge is how to connect the circuit DSSH together and how to implement accurately this circuit strategy for maximizing the total output power. The network topology DSSH extracted power a technique allows again up to in terms of maximal power output compared with network topology standard extracted at the resonant frequency. The simulation results show that by using the same input parameters the maximum efficiency for topology DSSH in parallel produces 120% more energy than topology DSSH-series. In addition, the energy harvesting by mono-DSSH is more than DSSH-series by 650% and it has exceeded DSSHind by 240%.
Improving the design of super-lift Luo converter using hybrid switching capac...nooriasukmaningtyas
In this article, an improvement to the positive output super-lift Luo converter (POSLC) has been proposed to get high gain at a low duty cycle. Also, reduce the stress on the switch and diodes, reduce the current through the inductors to reduce loss, and increase efficiency. Using a hybrid switch unit composed of four inductors and two capacitors it is replaced by the main inductor in the elementary circuit. It’s charged in parallel with the same input voltage and discharged in series. The output voltage is increased according to the number of components. The gain equation is modeled. The boundary condition between continuous conduction mode (CCM) and discontinuous conduction mode (DCM) has been derived. Passive components are designed to get high output voltage (8 times at D=0.5) and low ripple about (0.004). The circuit is simulated and analyzed using MATLAB/Simulink. Maximum power point tracker (MPPT) controls the converter to provide the most interest from solar energy.
Third harmonic current minimization using third harmonic blocking transformernooriasukmaningtyas
Zero sequence blocking transformers (ZSBTs) are used to suppress third harmonic currents in 3-phase systems. Three-phase systems where singlephase loading is present, there is every chance that the load is not balanced. If there is zero-sequence current due to unequal load current, then the ZSBT will impose high impedance and the supply voltage at the load end will be varied which is not desired. This paper presents Third harmonic blocking transformer (THBT) which suppresses only higher harmonic zero sequences. The constructional features using all windings in single-core and construction using three single-phase transformers explained. The paper discusses the constructional features, full details of circuit usage, design considerations, and simulation results for different supply and load conditions. A comparison of THBT with ZSBT is made with simulation results by considering four different cases
Power quality improvement of distribution systems asymmetry caused by power d...nooriasukmaningtyas
With an increase of non-linear load in today’s electrical power systems, the rate of power quality drops and the voltage source and frequency deteriorate if not properly compensated with an appropriate device. Filters are most common techniques that employed to overcome this problem and improving power quality. In this paper an improved optimization technique of filter applies to the power system is based on a particle swarm optimization with using artificial neural network technique applied to the unified power flow quality conditioner (PSO-ANN UPQC). Design particle swarm optimization and artificial neural network together result in a very high performance of flexible AC transmission lines (FACTs) controller and it implements to the system to compensate all types of power quality disturbances. This technique is very powerful for minimization of total harmonic distortion of source voltages and currents as a limit permitted by IEEE-519. The work creates a power system model in MATLAB/Simulink program to investigate our proposed optimization technique for improving control circuit of filters. The work also has measured all power quality disturbances of the electrical arc furnace of steel factory and suggests this technique of filter to improve the power quality.
Studies enhancement of transient stability by single machine infinite bus sys...nooriasukmaningtyas
Maintaining network synchronization is important to customer service. Low fluctuations cause voltage instability, non-synchronization in the power system or the problems in the electrical system disturbances, harmonics current and voltages inflation and contraction voltage. Proper tunning of the parameters of stabilizer is prime for validation of stabilizer. To overcome instability issues and get reinforcement found a lot of the techniques are developed to overcome instability problems and improve performance of power system. Genetic algorithm was applied to optimize parameters and suppress oscillation. The simulation of the robust composite capacitance system of an infinite single-machine bus was studied using MATLAB was used for optimization purpose. The critical time is an indication of the maximum possible time during which the error can pass in the system to obtain stability through the simulation. The effectiveness improvement has been shown in the system
Renewable energy based dynamic tariff system for domestic load managementnooriasukmaningtyas
To deal with the present power-scenario, this paper proposes a model of an advanced energy management system, which tries to achieve peak clipping, peak to average ratio reduction and cost reduction based on effective utilization of distributed generations. This helps to manage conventional loads based on flexible tariff system. The main contribution of this work is the development of three-part dynamic tariff system on the basis of time of utilizing power, available renewable energy sources (RES) and consumers’ load profile. This incorporates consumers’ choice to suitably select for either consuming power from conventional energy sources and/or renewable energy sources during peak or off-peak hours. To validate the efficiency of the proposed model we have comparatively evaluated the model performance with existing optimization techniques using genetic algorithm and particle swarm optimization. A new optimization technique, hybrid greedy particle swarm optimization has been proposed which is based on the two aforementioned techniques. It is found that the proposed model is superior with the improved tariff scheme when subjected to load management and consumers’ financial benefit. This work leads to maintain a healthy relationship between the utility sectors and the consumers, thereby making the existing grid more reliable, robust, flexible yet cost effective.
Energy harvesting maximization by integration of distributed generation based...nooriasukmaningtyas
The purpose of distributed generation systems (DGS) is to enhance the distribution system (DS) performance to be better known with its benefits in the power sector as installing distributed generation (DG) units into the DS can introduce economic, environmental and technical benefits. Those benefits can be obtained if the DG units' site and size is properly determined. The aim of this paper is studying and reviewing the effect of connecting DG units in the DS on transmission efficiency, reactive power loss and voltage deviation in addition to the economical point of view and considering the interest and inflation rate. Whale optimization algorithm (WOA) is introduced to find the best solution to the distributed generation penetration problem in the DS. The result of WOA is compared with the genetic algorithm (GA), particle swarm optimization (PSO), and grey wolf optimizer (GWO). The proposed solutions methodologies have been tested using MATLAB software on IEEE 33 standard bus system
Intelligent fault diagnosis for power distribution systemcomparative studiesnooriasukmaningtyas
Short circuit is one of the most popular types of permanent fault in power distribution system. Thus, fast and accuracy diagnosis of short circuit failure is very important so that the power system works more effectively. In this paper, a newly enhanced support vector machine (SVM) classifier has been investigated to identify ten short-circuit fault types, including single line-toground faults (XG, YG, ZG), line-to-line faults (XY, XZ, YZ), double lineto-ground faults (XYG, XZG, YZG) and three-line faults (XYZ). The performance of this enhanced SVM model has been improved by using three different versions of particle swarm optimization (PSO), namely: classical PSO (C-PSO), time varying acceleration coefficients PSO (T-PSO) and constriction factor PSO (K-PSO). Further, utilizing pseudo-random binary sequence (PRBS)-based time domain reflectometry (TDR) method allows to obtain a reliable dataset for SVM classifier. The experimental results performed on a two-branch distribution line show the most optimal variant of PSO for short fault diagnosis.
A deep learning approach based on stochastic gradient descent and least absol...nooriasukmaningtyas
More than eighty-five to ninety percentage of the diabetic patients are affected with diabetic retinopathy (DR) which is an eye disorder that leads to blindness. The computational techniques can support to detect the DR by using the retinal images. However, it is hard to measure the DR with the raw retinal image. This paper proposes an effective method for identification of DR from the retinal images. In this research work, initially the Weiner filter is used for preprocessing the raw retinal image. Then the preprocessed image is segmented using fuzzy c-mean technique. Then from the segmented image, the features are extracted using grey level co-occurrence matrix (GLCM). After extracting the fundus image, the feature selection is performed stochastic gradient descent, and least absolute shrinkage and selection operator (LASSO) for accurate identification during the classification process. Then the inception v3-convolutional neural network (IV3-CNN) model is used in the classification process to classify the image as DR image or non-DR image. By applying the proposed method, the classification performance of IV3-CNN model in identifying DR is studied. Using the proposed method, the DR is identified with the accuracy of about 95%, and the processed retinal image is identified as mild DR.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
We all have good and bad thoughts from time to time and situation to situation. We are bombarded daily with spiraling thoughts(both negative and positive) creating all-consuming feel , making us difficult to manage with associated suffering. Good thoughts are like our Mob Signal (Positive thought) amidst noise(negative thought) in the atmosphere. Negative thoughts like noise outweigh positive thoughts. These thoughts often create unwanted confusion, trouble, stress and frustration in our mind as well as chaos in our physical world. Negative thoughts are also known as “distorted thinking”.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
Chapter 3 - Islamic Banking Products and Services.pptx
The IoT and registration of MRI brain diagnosis based on genetic algorithm and convolutional neural network
1. Indonesian Journal of Electrical Engineering and Computer Science
Vol. 25, No. 1, January 2022, pp. 273~280
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v25.i1.pp273-280 273
Journal homepage: http://ijeecs.iaescore.com
The IoT and registration of MRI brain diagnosis based on
genetic algorithm and convolutional neural network
Ahmed Shihab Ahmed1
, Hussein Ali Salah2
1
Department of Basic Sciences, College of Nursing, University of Baghdad, Baghdad, Iraq
2
Department of Computer Systems, Technical Institute-Suwaira, Middle Technical University, Baghdad, Iraq
Article Info ABSTRACT
Article history:
Received Jul 16, 2021
Revised Oct 24, 2021
Accepted Nov 26, 2021
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.
Keywords:
Arduino global system for
mobile
Convolution neural network
Discrete wavelet transform
Genetic algorithm
Internet of things
Registration of magnetic
resonance imaging brain
This is an open access article under the CC BY-SA license.
Corresponding Author:
Hussein Ali Salah
Department of Computer Systems, Technical Institute-Suwaira, Middle Technical University
Muasker Al Rashid Street, Baghdad, Iraq
Email: hussein_tech@mtu.edu.iq
1. INTRODUCTION
Currently, medical imaging systems have a crucial role in the clinical workflow, due to their ability to
reflect anatomical and physiological features which are not otherwise available for inspection [1], [2]. Medical
image technology uses a variety of different concepts to quantify the spatial distributions of the physical
characteristics of humans and help to better understanding to complex or unusual diseases. Data processing is
essential for computer assistance medical diagnose [3], [4]. The method to integrate complementary
information from more than one image of a certain organ into one composite image can provide useful
information. The number of available modalities and the data volume of data in medical images makes it very
difficult to explicitly use them at different levels of complementary data [5], [6]. Moreover, each method
offers a partial amount of knowledge, and often two or more modes from the same patient are employed to get
well-understood sensed material. The first one can provide decent structural details (i.e. brilliant contrast to the
bones) is computed tomography (CT) scanner, while the magnetic resonance imaging (MRI) provides good
data on weak tissue (soft tissue). Two modalities are frequently used in brain visualization (such as white
matter and grey matter [7]-[9]. The word ‘‘registration’’ illustrates that is, finding a match between two image
registration is used to determine geometric transitions to provide a normal or reference image in the created
2. ISSN: 2502-4752
Indonesian J Elec Eng & Comp Sci, Vol. 25, No. 1, January 2022: 273-280
274
image [10], [11]. The technique of registration of images can be divided into three types, the optimization of
similarity measures, geometric transformation, and interpolation. The measure of similarity represents the key
step in the recording of images [12]-[14]. The registration procedure is of immense importance. MRI is
currently the most important way of obtaining soft tissue imaging especially in oncology, since the image
contrasts and resolution of lesions and healthy tissue are significantly improved [15], [16]. The MRI is
considered to be more accurate to assess the level of cancer infiltration than computed tomography [17]-[19].
The registration of biomedical images has many approaches, gold standard uses region-of-interest markers,
and other methods include correlation of geometrical characteristics [20], [21]. Intensity-based methods are
more worked in recent years to quantify correlations between an image with the intensity values (color or gray
level). The consistency of recording medical images depends on the options made using the method of
processing, interpolation, similarity calculation, and optimization. A specific use of the genetic algorithm is the
primary original characteristic of the method (from encoding to genetic space screening) [22], [23]. Genetic
algorithm (GA) relies upon ‘‘survival of the fittest’’ principle and a global selection of the best for the new
generation by crossover and mutation operators select the world's best new generation. The optimization
scheme is initialized by updating the generations with a random population of solutions and searches for
optima [24], [25]. Neural networks are playing a significant part in medical diagnosis and classification of
brain and tumors diseases. The neural network methods were implemented to relay the neural architecture of
the image segmentation network, also a hybrid image segmentation neural network with fuzzy [26], [27].
The main motivations of this work is incremental growth in the internet of things (IoT) technology
to be anywhere, anytime results in increasing demand for automation in e-health. The need for automatic
diagnosis applications with less time complexity and accuracy is highly preferred. Big data and data science
are a new hot topic addressed by soft computing techniques for their applicability to deal with vagueness and
uncertain data besides learning capability. The objectives of this work to develop a transmission model for the
IoT environment based on the cellular network that enables clinical diagnostic automation. The main
contributions is developing a MRI algorithm based on wavelet and fusion technology inside GA with
convolution neural network (CNN) for detection high accuracy of the proposed work. The main problem of
work is introduce automatic system for detection and daignosis MRI brain with high accuracy. In this study, a
hybrid system was proposed, which consists of two stages, the first stage is image registration that includes the
genetic algorithm, and the second stage is image detection that includes CNN and connected in by using global
system for mobile (GSM8080) for send massage to patient an IoT environment. This work aims to develop a
soft computing model for image registration as a first stage in the automatic diagnosis system. Then, it
proposes and incorporates a detection stage to automate the diagnosis process, which will prove the accuracy
of the proposed registration stage in the clinical workflow based on the IoT environment.
2. RELATED WORKS
Anaraki et al. [28], proposed a CNN-based method and genetic algorithm for classifying various
grading of glioma by MRI. In the proposed method, CNN's architecture is developed by the use of a genetic
algorithm, as opposed to current techniques of selection the (DNN) architecture, which relies upon on trial
and error or through the adoption common structures that are defined in advance. Furthermore, to minimize
prediction error variance, bagging as an ensemble algorithm was used on the optimum model that genetic
algorithm developed. To indicate the results briefly, in one case study, a 90.9% accuracy is gotten to classify
three grades of glioma in different case study, Pituitary, Meningioma, and Glioma tumor types are
categorized with the total accuracy at 94.2%. Shahamat and Abadeh [29], introduced 3D-CNN for classifying
brain magnetic resonance imaging into two pre-determined classifications. Moreover, a method of genetic
algorithm based brain masking was suggested as a visualization technique providing a clear understanding to
three-dimension convolutional neural network function. This method is composed two steps. In the first one,
a set of brain MRI scans will be utilized for training the three-dimension convolutional neural network. In the
second one, a genetic algorithm is implemented to detect brain regions in MRI scanning. The regions are
brain areas mostly used by 3D-CNN for extracting significant and discriminative traits from these areas. To
apply GA to magnetic resonance imaging scans of brain, a new approach of chromosomal encoding is
suggested. Furthermore, an evaluation is conducted to this proposed framework by the use Alzheimer's
disease Neuroimaging initiative (ADNI) (including one hundred forty individuals to disease classification of
Alzheimer) and autism brain imaging data exchange (ABIDE) (including one thousand individuals for
Autism classification) brain MRI datasets. Experimental results showed a five-fold classification accuracy of
0.70 for the dataset of Autism brain imaging data exchange and 0.85 for the dataset of Alzheimer's disease
Neuroimaging initiative. Those regions are interpreted as brain segments, which 3D-CNN typically uses to
extract features to classify brain diseases. Experimental results showed that along with interpretability of
3. Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752
The IoT and registration of magnetic resonance imaging brain diagnosis based on … (Ahmed Shihab Ahmed)
275
model, this method increases the classification model's final performance in number of cases concerning the
parameters of the model
Sajjad et al. [30] introduced multi-grade brain tumor classification system based CNN. Firstly:
segmenting tumor regions from images of magnetic resonance imaging by the use of deep learning technique.
Secondly: augmenting data widely can be used to train the system proposed in order to avoid any problem
related to lacking data when handling with MRI to classify multi-graded brain tumors. Thirdly: a pre-trained
CNN model is fine-tuned using augmented data for brain tumor grade classification. Thirdly, CNN model
trained in advance is fine-tuned using by the use of augmented data to classify the degree of brain tumor.
Chang et al. [31] information related to MRI and molecular data, for 259 patients, from cancer
imaging archives were obtained, those individuals were having glioma, either high or low-grade. CNN was
trained for classifying 1p/19q codeletion, isocitrate dehydrogenase 1 (IDH1) mutation status, and O6-
methylguanine-DNA methyltransferase (MGMT) promoter hypermethylation status. Principal component
analysis of the final convolutional neural network layer was used to extract the key imaging features to
classify cases accurately. Results: the process of classification is highly accurate: IDH1 mutation status, 94%.
The authors Rahman et al. [32] implemented IoT to facilitate farming, particularly for those who want a
smart approach to agriculture. This study focuses on real-time surveillance with the low cost-effective
security solution. Make the most of computer resources such encryption and decryption time, battery usage,
and so on, divide the data utilized in the IoT environment into three categories of sensitivity: low, medium,
and high sensitive data [33]. In this paper, a framework is provided for encrypting data based on the level of
sensitivity utilizing machine learning K-nearest neighbors (K-NN). Tanh et al. [34] enhanced security
protocols presented a viable solution for comprehensive protection of IoT systems from network security
assaults. Algorithmic enhancement favorably contributes to this crucial work by combining security solutions
on the levels of the IoT with code optimization. Also, enhance and combine the DTLS Protocol with the
overhearing mechanism, and then conduct tests to demonstrate effectiveness, feasibility, cost-efficiency, and
applicability on popular IoT network models. Presents NB-IoT testing approach that is tailored to the local
radio network planning requirements [35]. Adducing the major findings about the viability of employing an
in-band scenario for deploying NB-IoT over a 4G network in a suburban setting based on the acquired data.
Rajbongshi et al. [36], Erwin et al. [37] suggested different types of leaf diseases, such as anthracnose, gall
machi, powdery mildew, and red rust, are employed in the dataset, which includes 1500 photos of damaged
and healthy mango leaves. A new category has been added to the dataset. Also looked at the overall
performance matrices and discovered that the DenseNet201 beats other models by achieving the highest
accuracy of 98.00%. Fadil et al. [38] The medical images are enhanced using the fuzzy C-means clustering
(FCM) approach. There are two stages to the enhancing procedure. On the picture pixels, the suggested
technique performs a cluster test. The difference in gray level between the various items is then increased to
achieve the medical picture enhancing goal. Various photos were used to test the experimental outcomes.
3. THE PROPOSED MODEL COMPONENTS OF MRI BRAIN DIAGNOSIS
In this proposed work, the genetic algorithm and CNN are used to determine the brain tumor
classification based on the principle of registration and this is achieved by loading the (source and reference)
image. After that, image is processed in regard with smoothing, reducing noise, by using Gaussian filter.
Genetic algorithm is also applied to achieve the principle of registration, then, CNN is used to classify the
brain tumor. Eventually, sending a massage to a patient explaining the tumor grade depending on GSM
Arduino to achieve the principle of IoT, as shown in Figure 1 and Figure 2.
Figure 1. Describe the proposed approach of work
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Figure 2. The work flow of MRI brain diagnosis
4. MRI BRAIN DIAGNOSIS SYSTEM IMPLEMENTATION
This work proposed the automatic model to detect the brain tumor and send a message to patient,
that can achieved by using MATLAB 2020a and Arduino with GSM8080. It uses database from Medical
City Hospital in Baghdad, for 80 patients, (800) images are diagnosed to two classes normal 55 persons and
35 patients. The source image and reference image are loaded as shown in Figure 3, the genetic algorithm is
applied to achieve the registration, then the output of genetic algorithm is subtracted from source MRI image
then, the database is divided to 80 training and 20 testing based on cross-validation. In addition to, the CNN
is applied to classify the image and send it to patient by GSM as shown, in Figures 3-5.
Figure 3. The Gui of Matlab show the result of proposed work
Figure 4. The Arduino and GSM are connected to Matlab
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Figure 5. The message sent to patient
The CNN training model is introduced, the volume of entered MRI is 100*100* 31, and training setup of
CNN works, as shown here, momentum is 0.9 and learning rate is 0.001 and the architecture of network is
composed 4 pooling layers and 4 convolution layers and, two fully connected layers follow those layers. A Relu
layer comes after a convolution layer, an activation function for improving the CNNs performance. In the network
training, regularization with the weight decay five×ten−four was used. Initially, the learning rate was set to 0.001,
the training was stopped after 1000 epoch, and the dropout ratio was set to zero.0, as shown in Table 1.
Table 1. Analysis result of CNN model
Layer Name Activations Learnable
1 Image input 100*100*1 -
2 Convolution1 96*96*1
Weight 5*5*1*20,
Bias 1*1*20
3 Relu1 96*96*1 -
4 Pool max1 48*48*20 -
5 Convolution2 44*44*20
Weight 5*5*20*20
Bias 1*1*20
6 Relu2 44*44*20 -
7 Pool max2 22*22*20 -
8 Fully Connected Layer 1*1*1024 -
9 Fully Connected Layer 1*1*256 -
10 Fully Connected Layer 1*1*2 -
11 SoftMax Layer 1*1*2 -
12 Classification Layer - -
After building the network architecture as shown in Table 2, the hybrid Mamdani fuzzy and CNN
train model starts in epoch (1), the parameter of Elapsed time is 2 second, parameter of accuracy is 28.13%
and the parameter of mini batch loss is 1.4149. At the epoch 14 the parameter of accuracy reached to 92.19%,
parameter of mini batch loss 0.2024 and the elapsed time is 05:41 minute. At the epoch 28 the parameter of
accuracy reached to 99.22%, parameter of mini batch loss 0.0576 and the elapsed time is 11:08 minute. At
the epoch 41 the parameter of accuracy reached to 100%, parameter of mini batch loss 0.0021 and the
elapsed time is 16.32 minute.
After training the models for recognition of a brain tumor, the classification results are as shown in
Table 3, a detailed classification of the test samples is listed. The true and reference columns represent the
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true situation, while the row values are the predicted true, the model or the model has to predict false as
shown in Table 3. In Table 4 present the compare the proposed work with other researcher.
Table 2. Show the train on CNN
Epoch Iteration
Time Elapsed
(hh:mm: ss)
Mini-batch
Accuracy
Mini-batch
loss
Base Learning
Rate
1 1 00:00:02 28.13 % 1.4149 0.0010
5 50 00:01:52 46.06 % 0.9088 0.0010
10 100 00:03:43 84.38 % 0.4804 0.0010
14 150 00:05:41 92.19 % 0.2024 0.0010
19 200 00:07:32 98.44 % 0.1102 0.0010
23 250 00:09:20 86.72 % 0.3928 0.0010
28 300 00:11:08 99.22 % 0.0576 0.0010
32 350 00:12:56 100.00 % 0.0936 0.0010
37 400 00:14:44 100.00 % 0.0361 0.0010
41 450 00:16:32 100.00 % 0.0021 0.0010
46 500 00:18:20 100.00 % 0.0003 0.0010
50 550 00:20:03 100.00 % 0.0005 0.0010
55 600 00:21:46 100.00 % 0.0003 0.0010
60 650 00:23:41 100.00 % 0.0003 0.0010
64 700 00:25:39 100.00 % 0.0002 0.0010
69 750 00:27:31 100.00 % 0.0002 0.0010
73 800 00:29:24 100.00 % 0.0003 0.0010
78 850 00:31:18 100.00 % 0.0002 0.0010
82 900 00:33:12 100.00 % 0.0002 0.0010
Noted:
Training on single CPU
Initialization image normalization
Table 3. Test phase statistic measures for the CNN
Statistic Description CNN
Accuracy Rate of correctly predicted
ACC= TP+ TN / (TP+ TN+ FP+ FN)
98.88%
True positive Number of correctly predicted. 55
True Negative Number of malicious object which are correctly classified 34
False positive Number of incorrectly predicted 0
False Negative Number of malicious object which are incorrectly predicted 4
Misclassification Rate the percentage of incorrectly predicted
Misclassification Rate =(FP+FN)/total
1.12
Specificity calculated as the number of correct negative predictions Specificity= TN/(TN+FP) 0.9814
Precision calculated as the number of correct positive
Precision =TP /(TP+FP)
1
Table 4. Compare the proposed work with other work
Author Accuracy Methods
Anaraki et al. [28] 94.2% GA-CNN
Zacharaki et al. [39] 85% Svm+Knn
Cheng et al. [40] 91.28% Svm+Knn
Paul et al. [41] 91.43% CNN
Afshar et al. [42] 90.89% CNN
Ertosun and Rubin [43] 96% CNN
Sultan et al. [44] 96.13 CNN
Chandra and Bajpai [45] - fractional filter (mask design) for benign brain tumor detection
Swati et al. [46] 94.82% pre-trained deep CNN model and propose a block-wise fine-tuning strategy
based on transfer learning
Proposed work 98.8% Genetic Algorithm and Convolution Neural Network
5. CONCLUSION
This work proposes building automatic IoT to detect and classify brain MRI based on deep learning
and arduino GSM. Moreover, the principle of registration is applied to MRI using genetic algorithm, as
following, reading the source image and loading the reference mage, reducing the noise of MRI image by
bilateral filter, the genetic algorithm is applied to obtain the best fusion image from source and reference
image, computing the similarly by subtracting the result of registration image to get the best feature of image,
CNN is applied to classify brain tumor, and sending message to patient by GSM. The proposed model is
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tested on MRI Medical City Hospital in Baghdad, database consists of 550 normal and 350 abnormal images
and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy. In the future
work we can apply the IoT technique and registration of skin cancer based on K-means cluster and self
organizing maps by using a data set of medical images.
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BIOGRAPHIES OF AUTHORS
Ahmed Shihab Ahmed is a computer scientist specialized in the field of image
processing and decision support systems. He received the four-year B.Sc. degree in Computer
Science in 2000 from Al-Rafidain University College, Iraq. In 2015, he concluded a Master in
Computer Science (MCS) from Middle East University, Jordan. He has been working as a
programmer at University of Baghdad from 2004 until 2014 and then worked as an assistant
lecturer at University of Baghdad from 2015 until now. His main research interests include:
artificial neural network, image processing, decision support systems. He can be contacted at
email: ahmedshihabinfo@conursing.uobaghdad.edu.iq.
Hussein Ali Salah received the four-year B.Sc. degree in Computer Science in 2000
from Al- Rafidain University College, Iraq. In 2004, he concluded a Master in Computer Science
(MCS) from Baghdad University, college of science. He received the Ph.D. degree in Computer
Science IT in 2016 from Politehnica’ University of Bucharest, Bucharest, Romania. His main
research interests include data mining, decision support system, web design and intelligent DSS.
He has worked as a head of the computer systems department, Middle Technical University,
Technical Institute-Suwaira, Wasit/Iraq from 2016 until now. He can be contacted at email:
hussein_tech@mtu.edu.iq.