Primary challenges are the identification, segmentation, and extraction of the afflicted area from the scanning of magnetic resonance. However, it is a time-consuming and tiresome for clinical specialists. In this paper,
an automated brain tumor system is proposed. The proposed system employs hybrid image processing techniques such as contrast correction, histogram normalization, thresholding techniques, arithmetic, and morphological operations to quarantine nearby organs and other tissue from the brain for improving the localization of the affected region. At first, the skull stripping process is proposed to segregate the non-designated regions to extract the designated brain regions. Those resultant brain region images are further subjected to discover the brain tumor. The planned scheme is studied on the magnetic resonance (MR) images with the use of T1, T2, T1c, and fluid-attenuated inversion recovery (FLAIR). The proposed hybrid method employed. The results reveal that the proposed method is quite efficient to extract the tumor region. The accuracy rate for segmentation and separation of area of interest in brain tumor reached to 95%. Finally, the significance of the proposed procedure is confirmed using the real image clinical dataset got from ten patients were diagnosed as begin, malignant, and metastatic brain tumors in Al-Yarmouk and Baghdad teaching hospital in Baghdad, Iraq.
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.
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
A Review Paper on Automated Brain Tumor DetectionIRJET Journal
This document reviews several studies that used deep learning techniques to detect brain tumors using MRI images. It summarizes key papers that used algorithms like VGG-16, ResNet, Inception, Fast R-CNN and more. VGG-16 achieved accuracies of 75.18-89.45% for detecting glioma, meningioma and pituitary tumors. ResNet-50 and Inception-V3 also performed well with 96% accuracy. Later papers compared methods like AlexNet, GoogleNet and ResNet, with VGG-16 achieving the highest accuracy of 98.69%. More recent multi-channel approaches using DenseNet201, ResNet-50 and SRN obtained 98.31% accuracy. Overall, deep learning has shown good results for brain
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.
DIRECTIONAL CLASSIFICATION OF BRAIN TUMOR IMAGES FROM MRI USING CNN-BASED DEE...IRJET Journal
This document presents research on using a convolutional neural network (CNN) model for the detection and classification of brain tumors from MRI images. The CNN model improves the accuracy of tumor detection and can serve as a useful tool for physicians. The researchers trained and tested several CNN architectures, including CNN, ResNet50, MobileNetV2, and VGG19 on an MRI brain image database. Their proposed model uses a modified Residual U-Net architecture with residual blocks and attention gates to better segment tumors and extract local features from MRI images. Evaluation results found their model achieved better accuracy than existing methods like U-Net and CNN for brain tumor segmentation tasks.
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.
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.
Brain Tumor Detection and Segmentation using UNETIRJET Journal
This document discusses brain tumor detection and segmentation using the UNET model. It analyzes previous research on brain tumor segmentation techniques and their limitations. The proposed method uses the BraTS 2020 dataset containing 369 MRI images for training and 125 for testing. It develops a 3D UNET model for multimodal brain tumor segmentation. The model generates 3D outputs and achieves 98.5% accuracy in segmenting whole, core and enhancing tumors.
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.
Automatic brain tumor detection using adaptive region growing with thresholdi...IAESIJAI
Brain cancer affects many people around the world. It's not just limited to the elderly; it is also recognized in children. With the development of image processing, early detection of mental development is possible. Some designers suggest deformable models, histogram averaging, or manual division. Due to constant manual intervention, these cycles can be uncomfortable and tiring. This research introduces a high-level system for the removal of malignant tumors from attractive reverberation images, based on a programmed and rapid distribution strategy for surface extraction and recreation for clinicians. To test the proposed system, acquired tomography images from the Cancer Imaging Archive were used. The results of the study strongly demonstrate that the intended structure is viable in brain tumor detection.
A Review Paper on Automated Brain Tumor DetectionIRJET Journal
This document reviews several studies that used deep learning techniques to detect brain tumors using MRI images. It summarizes key papers that used algorithms like VGG-16, ResNet, Inception, Fast R-CNN and more. VGG-16 achieved accuracies of 75.18-89.45% for detecting glioma, meningioma and pituitary tumors. ResNet-50 and Inception-V3 also performed well with 96% accuracy. Later papers compared methods like AlexNet, GoogleNet and ResNet, with VGG-16 achieving the highest accuracy of 98.69%. More recent multi-channel approaches using DenseNet201, ResNet-50 and SRN obtained 98.31% accuracy. Overall, deep learning has shown good results for brain
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.
DIRECTIONAL CLASSIFICATION OF BRAIN TUMOR IMAGES FROM MRI USING CNN-BASED DEE...IRJET Journal
This document presents research on using a convolutional neural network (CNN) model for the detection and classification of brain tumors from MRI images. The CNN model improves the accuracy of tumor detection and can serve as a useful tool for physicians. The researchers trained and tested several CNN architectures, including CNN, ResNet50, MobileNetV2, and VGG19 on an MRI brain image database. Their proposed model uses a modified Residual U-Net architecture with residual blocks and attention gates to better segment tumors and extract local features from MRI images. Evaluation results found their model achieved better accuracy than existing methods like U-Net and CNN for brain tumor segmentation tasks.
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.
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.
Brain Tumor Detection and Segmentation using UNETIRJET Journal
This document discusses brain tumor detection and segmentation using the UNET model. It analyzes previous research on brain tumor segmentation techniques and their limitations. The proposed method uses the BraTS 2020 dataset containing 369 MRI images for training and 125 for testing. It develops a 3D UNET model for multimodal brain tumor segmentation. The model generates 3D outputs and achieves 98.5% accuracy in segmenting whole, core and enhancing tumors.
Hybrid model for detection of brain tumor using convolution neural networksCSITiaesprime
The development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisions because of this forecast. The proposed work creates a hybrid convolution neural networks (CNN) model using CNN for feature extraction and logistic regression (LR). The pre-trained model visual geometry group 16 (VGG16) is used for the extraction of features. To reduce the complexity and parameters to train we eliminated the last eight layers of VGG16. From this transformed model the features are extracted in the form of a vector array. These features fed into different machine learning classifiers like support vector machine (SVM), naïve bayes (NB), LR, extreme gradient boosting (XGBoost), AdaBoost, and random forest for training and testing. The performance of different classifiers is compared. The CNN-LR hybrid combination outperformed the remaining classifiers. The evaluation measures such as recall, precision, F1-score, and accuracy of the proposed CNN-LR model are 94%, 94%, 94%, and 91% respectively.
Brain tumor visualization for magnetic resonance images using modified shape...IJECEIAES
3D visualization plays an essential role in medical diagnosis and setting treatment plans especially for brain cancer. There have been many attempts for brain tumor reconstruction and visualization using various techniques. However, this problem is still considered unsolved as more accurate results are needed in this critical field. In this paper, a sequence of 2D slices of brain magnetic resonance Images was used to reconstruct a 3D model for the brain tumor. The images were automatically segmented using wavelet multi-resolution expectation maximization algorithm. Then, the inter-slice gaps were interpolated using the proposed modified shape-based interpolation method. The method involves three main steps; transferring the binary tumor images to distance images using a suitable distance function, interpolating the distance images using cubic spline interpolation and thresholding the interpolated values to get the reconstructed slices. The final tumor is then visualized as a 3D isosurface. We evaluated the proposed method by removing an original slice from the input images and interpolating it, the results outperform the original shape-based interpolation method by an average of 3% reaching 99% of accuracy for some slice images.
BRAIN TUMOR DETECTION USING CNN & ML TECHNIQUESIRJET Journal
1) The document proposes two methods for detecting brain tumors using MRI images - one using traditional machine learning classifiers after segmentation with FCM and feature extraction, and one using a convolutional neural network.
2) For the first method, MRI images undergo preprocessing like skull stripping and noise removal before segmentation with Fuzzy C-Means clustering and morphological operations. Features are then extracted and classified with models like KNN, logistic regression, random forest.
3) For the second method, a 5-layer CNN is used to directly classify tumor images. The CNN includes convolutional, max pooling, flatten, and dense layers to reduce parameters and detect tumors with 92.42% accuracy.
IRJET- Brain Tumor Detection using Convolutional Neural NetworkIRJET Journal
This document summarizes research on using convolutional neural networks (CNNs) to detect brain tumors from MRI images. It begins with an abstract describing how earlier tumor detection was done manually by doctors, which took more time and was sometimes inaccurate. CNN models provide quicker and more precise results. The document then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. It proposes using a CNN-based classifier to overcome these limitations by comparing trained and test data to get the best results. Key steps in tumor detection using image processing techniques are described as image pre-processing, segmentation, feature extraction, and classification.
A Wavelet Based Automatic Segmentation of Brain Tumor in CT Images Using Opti...CSCJournals
This document summarizes a research paper that proposes a new method for automatically segmenting brain tumors in CT images. The method uses a combination of wavelet-based texture features extracted from discrete wavelet transformed sub-bands. These features are optimized using genetic algorithms and used to train probabilistic neural network and feedforward neural network classifiers to segment tumors. The proposed method is evaluated on brain CT images and shown to outperform existing segmentation methods.
Brain Tumor Detection and Classification Using MRI Brain ImagesIRJET Journal
This document presents research on detecting and classifying brain tumors using MRI images. It discusses:
1) Using k-means clustering for pre-processing MRI images to reduce noise and increase detection accuracy. Marker-controlled watershed transformation and grey-level co-occurrence matrix are then used for tumor detection and feature extraction.
2) Two classification methods are employed: support vector machine (SVM) and artificial neural network (ANN). SVM and ANN have been shown to accurately classify tumors in an effective manner.
3) The paper proposes an algorithm to differentiate between benign and malignant tumors using watershed segmentation and extracting grey-level co-occurrence matrix features from MRI images, which are then classified using SVM and AN
A Review on Multiclass Brain Tumor Detection using Convolutional Neural Netwo...IRJET Journal
This document summarizes a review on using convolutional neural networks and support vector machines for multiclass brain tumor detection. It begins by introducing the importance of accurate brain tumor diagnosis and segmentation. It then describes the proposed 5-stage approach: 1) applying linear contrast stretching for edge detection, 2) developing a CNN architecture for segmentation, 3) using transfer learning from MobileNetV2 for feature extraction, 4) selecting optimal features using entropy control, and 5) classifying tumors into categories using multi-class SVM. Related works applying deep learning and machine learning methods for brain tumor detection, segmentation and classification are also summarized.
IRJET- Diversified Segmentation and Classification Techniques on Brain Tu...IRJET Journal
This document summarizes 20 research papers on techniques for detecting brain tumors using medical images like MRI scans. It discusses several techniques for image segmentation, feature extraction, and classification that have been used to automatically detect and diagnose brain tumors. The goal of the work is to consolidate these different techniques and provide new insights on recent approaches to brain tumor image processing. Key methods discussed include convolutional neural networks, random forest classifiers, discrete wavelet transforms, and probabilistic neural networks.
An effective feature selection using improved marine predators algorithm for ...IJECEIAES
Alzheimer’s disease (AD) is an irremediable neurodegenerative illness developed by the fast deterioration of brain cells. AD is mostly common in elder people and it extremely disturbs the physical and mental health of patients, therefore early detection is essential to prevent AD development. However, the precise detection of AD and mild cognitive impairment (MCI) is difficult during classification. In this paper, the Residual network i.e., ResNet-18 is used for extracting the features, and the proposed improved marine predators algorithm (IMPA) is developed for choosing the optimum features to perform an effective classification of AD. The multi-verse optimizer (MVO) used in the IMPA helps to balance exploration and exploitation, which leads to the selection of optimal relevant features. Further, the classification of AD is accomplished using the multiclass support vector machine (MSVM). Open access series of imaging studies-1 (OASIS-1) and Alzheimer disease neuroimaging initiative (ADNI) datasets are used to evaluate the IMPA-MSVM method. The performance of the IMPA-MSVM method is analyzed using accuracy, sensitivity, specificity, positive predictive value (PPV) and matthews correlation coefficient (MCC). The existing methods such as the deep learning-based segmenting method using SegNet (DLSS), mish activation function (MAF) with spatial transformer network (STN) and BrainNet2D are used to evaluate the IMPA-MSVM method. The accuracy of IMPA-MSVM for the ADNI dataset is 98.43% which is more when compared to the DLSS and MAF-STN.
A modified residual network for detection and classification of Alzheimer’s ...IJECEIAES
Alzheimer's disease (AD) is a brain disease that significantly declines a person's ability to remember and behave normally. By applying several approaches to distinguish between various stages of AD, neuroimaging data has been used to extract different patterns associated with various phases of AD. However, because the brain patterns of older adults and those in different phases are similar, researchers have had difficulty classifying them. In this paper, the 50-layer residual neural network (ResNet) is modified by adding extra convolution layers to make the extracted features more diverse. Besides, the activation function (ReLU) was replaced with (Leaky ReLU) because ReLU takes the negative parts of its input, drops them to zero, and retains the positive parts. These negative inputs may contain useful feature information that could aid in the development of high-level discriminative features. Thus, Leaky ReLU was used instead of ReLU to prevent any potential loss of input information. In order to train the network from scratch without encountering the issue of overfitting, we added a dropout layer before the fully connected layer. The proposed method successfully classified the four stages of AD with an accuracy of 97.49 % and 98 % for precision, recall, and f1-score.
This document reviews various automated techniques that have been developed for brain tumor detection. It summarizes research done by several researchers on methods like sequential floating forward selection, color coding schemes using brain atlases, neural networks, region growing segmentation combined with area calculation, symmetry analysis of tumor areas in MRI images, and combining clustering and classification algorithms. The paper concludes that image segmentation plays an important role in medical applications like tumor diagnosis and that more robust techniques are needed for high accuracy and reliability.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document presents a comparative study of two segmentation methods - k-means clustering and fuzzy c-means clustering with genetic algorithm - for detecting brain tumors in MRI images. K-means clustering is used to segment MRI images into clusters and identify tumor regions. Fuzzy c-means clustering with genetic algorithm aims to improve upon k-means by eliminating over-segmentation issues and providing faster, more efficient clustering results. The experimental results indicate fuzzy c-means performs better than k-means for brain tumor segmentation. The document also reviews several other related works applying techniques like edge detection and probabilistic neural networks to segment brain tumors from MRI scans.
A novel framework for efficient identification of brain cancer region from br...IJECEIAES
Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.
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.
This document outlines a project on brain tumor detection and diagnosis using convolutional neural networks. It discusses the objective of outlining current automatic segmentation techniques using CNNs. It then provides an introduction on the importance of accurate brain tumor segmentation for diagnosis and treatment. The remaining sections cover literature reviews on CNN segmentation methods, the overall architecture and working principles, applications and the future scope of this area of research.
IRJET- Brain Tumor Detection using Hybrid Model of DCT DWT and ThresholdingIRJET Journal
The document presents a new hybrid model for detecting brain tumors in MRI images. It uses a combination of discrete cosine transform (DCT), discrete wavelet transform (DWT), principal component analysis (PCA), and fuzzy c-means clustering. DCT and DWT are applied to extract features from MRI images. PCA is then used to reduce the dimensions of the extracted features. Finally, fuzzy c-means clustering is used to segment and detect tumors. The proposed hybrid model is evaluated using objective metrics like RMSE, PSNR, correlation, contrast and entropy. Results show the hybrid model achieves better values for these metrics compared to using DCT or DWT alone, indicating it more accurately detects and segments tumors in MRI images.
Segmentation and Classification of Lung Nodule in Chest Radiograph ImageIJTET Journal
Abstract-Image segmentation plays a vital step in medical image processing. Lung cancer is the largest cause of tumor deaths. Since the nodules are commonly attached to blood vessels, detection of lung nodules is the challenging task .By early detection the lung cancer can be completely recovered. Especially in the case of lung nodule detection Computer Aided Detection (CAD) is effective for the improvement of radiologists‟ diagnosis. In this paper an efficient lung nodule detection scheme is developed by performing nodule segmentation through Fuzzy C-Means (FCM) and Virtual Dual Energy (VDE). Here the input image is considered as an radiograph image, then the lung is segmented by using Multi segment Active Shape Model (MASM). Finally neural network classifies as a nodule or non-nodule candidates.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...IJEECSIAES
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Hybrid model for detection of brain tumor using convolution neural networksCSITiaesprime
The development of aberrant brain cells, some of which may turn cancerous, is known as a brain tumor. Magnetic resonance imaging (MRI) scans are the most common technique for finding brain tumors. Information about the aberrant tissue growth in the brain is discernible from the MRI scans. In numerous research papers, machine learning, and deep learning algorithms are used to detect brain tumors. It takes extremely little time to forecast a brain tumor when these algorithms are applied to MRI pictures, and better accuracy makes it easier to treat patients. The radiologist can make speedy decisions because of this forecast. The proposed work creates a hybrid convolution neural networks (CNN) model using CNN for feature extraction and logistic regression (LR). The pre-trained model visual geometry group 16 (VGG16) is used for the extraction of features. To reduce the complexity and parameters to train we eliminated the last eight layers of VGG16. From this transformed model the features are extracted in the form of a vector array. These features fed into different machine learning classifiers like support vector machine (SVM), naïve bayes (NB), LR, extreme gradient boosting (XGBoost), AdaBoost, and random forest for training and testing. The performance of different classifiers is compared. The CNN-LR hybrid combination outperformed the remaining classifiers. The evaluation measures such as recall, precision, F1-score, and accuracy of the proposed CNN-LR model are 94%, 94%, 94%, and 91% respectively.
Brain tumor visualization for magnetic resonance images using modified shape...IJECEIAES
3D visualization plays an essential role in medical diagnosis and setting treatment plans especially for brain cancer. There have been many attempts for brain tumor reconstruction and visualization using various techniques. However, this problem is still considered unsolved as more accurate results are needed in this critical field. In this paper, a sequence of 2D slices of brain magnetic resonance Images was used to reconstruct a 3D model for the brain tumor. The images were automatically segmented using wavelet multi-resolution expectation maximization algorithm. Then, the inter-slice gaps were interpolated using the proposed modified shape-based interpolation method. The method involves three main steps; transferring the binary tumor images to distance images using a suitable distance function, interpolating the distance images using cubic spline interpolation and thresholding the interpolated values to get the reconstructed slices. The final tumor is then visualized as a 3D isosurface. We evaluated the proposed method by removing an original slice from the input images and interpolating it, the results outperform the original shape-based interpolation method by an average of 3% reaching 99% of accuracy for some slice images.
BRAIN TUMOR DETECTION USING CNN & ML TECHNIQUESIRJET Journal
1) The document proposes two methods for detecting brain tumors using MRI images - one using traditional machine learning classifiers after segmentation with FCM and feature extraction, and one using a convolutional neural network.
2) For the first method, MRI images undergo preprocessing like skull stripping and noise removal before segmentation with Fuzzy C-Means clustering and morphological operations. Features are then extracted and classified with models like KNN, logistic regression, random forest.
3) For the second method, a 5-layer CNN is used to directly classify tumor images. The CNN includes convolutional, max pooling, flatten, and dense layers to reduce parameters and detect tumors with 92.42% accuracy.
IRJET- Brain Tumor Detection using Convolutional Neural NetworkIRJET Journal
This document summarizes research on using convolutional neural networks (CNNs) to detect brain tumors from MRI images. It begins with an abstract describing how earlier tumor detection was done manually by doctors, which took more time and was sometimes inaccurate. CNN models provide quicker and more precise results. The document then reviews several existing techniques for brain tumor segmentation and classification, noting their advantages and limitations. It proposes using a CNN-based classifier to overcome these limitations by comparing trained and test data to get the best results. Key steps in tumor detection using image processing techniques are described as image pre-processing, segmentation, feature extraction, and classification.
A Wavelet Based Automatic Segmentation of Brain Tumor in CT Images Using Opti...CSCJournals
This document summarizes a research paper that proposes a new method for automatically segmenting brain tumors in CT images. The method uses a combination of wavelet-based texture features extracted from discrete wavelet transformed sub-bands. These features are optimized using genetic algorithms and used to train probabilistic neural network and feedforward neural network classifiers to segment tumors. The proposed method is evaluated on brain CT images and shown to outperform existing segmentation methods.
Brain Tumor Detection and Classification Using MRI Brain ImagesIRJET Journal
This document presents research on detecting and classifying brain tumors using MRI images. It discusses:
1) Using k-means clustering for pre-processing MRI images to reduce noise and increase detection accuracy. Marker-controlled watershed transformation and grey-level co-occurrence matrix are then used for tumor detection and feature extraction.
2) Two classification methods are employed: support vector machine (SVM) and artificial neural network (ANN). SVM and ANN have been shown to accurately classify tumors in an effective manner.
3) The paper proposes an algorithm to differentiate between benign and malignant tumors using watershed segmentation and extracting grey-level co-occurrence matrix features from MRI images, which are then classified using SVM and AN
A Review on Multiclass Brain Tumor Detection using Convolutional Neural Netwo...IRJET Journal
This document summarizes a review on using convolutional neural networks and support vector machines for multiclass brain tumor detection. It begins by introducing the importance of accurate brain tumor diagnosis and segmentation. It then describes the proposed 5-stage approach: 1) applying linear contrast stretching for edge detection, 2) developing a CNN architecture for segmentation, 3) using transfer learning from MobileNetV2 for feature extraction, 4) selecting optimal features using entropy control, and 5) classifying tumors into categories using multi-class SVM. Related works applying deep learning and machine learning methods for brain tumor detection, segmentation and classification are also summarized.
IRJET- Diversified Segmentation and Classification Techniques on Brain Tu...IRJET Journal
This document summarizes 20 research papers on techniques for detecting brain tumors using medical images like MRI scans. It discusses several techniques for image segmentation, feature extraction, and classification that have been used to automatically detect and diagnose brain tumors. The goal of the work is to consolidate these different techniques and provide new insights on recent approaches to brain tumor image processing. Key methods discussed include convolutional neural networks, random forest classifiers, discrete wavelet transforms, and probabilistic neural networks.
An effective feature selection using improved marine predators algorithm for ...IJECEIAES
Alzheimer’s disease (AD) is an irremediable neurodegenerative illness developed by the fast deterioration of brain cells. AD is mostly common in elder people and it extremely disturbs the physical and mental health of patients, therefore early detection is essential to prevent AD development. However, the precise detection of AD and mild cognitive impairment (MCI) is difficult during classification. In this paper, the Residual network i.e., ResNet-18 is used for extracting the features, and the proposed improved marine predators algorithm (IMPA) is developed for choosing the optimum features to perform an effective classification of AD. The multi-verse optimizer (MVO) used in the IMPA helps to balance exploration and exploitation, which leads to the selection of optimal relevant features. Further, the classification of AD is accomplished using the multiclass support vector machine (MSVM). Open access series of imaging studies-1 (OASIS-1) and Alzheimer disease neuroimaging initiative (ADNI) datasets are used to evaluate the IMPA-MSVM method. The performance of the IMPA-MSVM method is analyzed using accuracy, sensitivity, specificity, positive predictive value (PPV) and matthews correlation coefficient (MCC). The existing methods such as the deep learning-based segmenting method using SegNet (DLSS), mish activation function (MAF) with spatial transformer network (STN) and BrainNet2D are used to evaluate the IMPA-MSVM method. The accuracy of IMPA-MSVM for the ADNI dataset is 98.43% which is more when compared to the DLSS and MAF-STN.
A modified residual network for detection and classification of Alzheimer’s ...IJECEIAES
Alzheimer's disease (AD) is a brain disease that significantly declines a person's ability to remember and behave normally. By applying several approaches to distinguish between various stages of AD, neuroimaging data has been used to extract different patterns associated with various phases of AD. However, because the brain patterns of older adults and those in different phases are similar, researchers have had difficulty classifying them. In this paper, the 50-layer residual neural network (ResNet) is modified by adding extra convolution layers to make the extracted features more diverse. Besides, the activation function (ReLU) was replaced with (Leaky ReLU) because ReLU takes the negative parts of its input, drops them to zero, and retains the positive parts. These negative inputs may contain useful feature information that could aid in the development of high-level discriminative features. Thus, Leaky ReLU was used instead of ReLU to prevent any potential loss of input information. In order to train the network from scratch without encountering the issue of overfitting, we added a dropout layer before the fully connected layer. The proposed method successfully classified the four stages of AD with an accuracy of 97.49 % and 98 % for precision, recall, and f1-score.
This document reviews various automated techniques that have been developed for brain tumor detection. It summarizes research done by several researchers on methods like sequential floating forward selection, color coding schemes using brain atlases, neural networks, region growing segmentation combined with area calculation, symmetry analysis of tumor areas in MRI images, and combining clustering and classification algorithms. The paper concludes that image segmentation plays an important role in medical applications like tumor diagnosis and that more robust techniques are needed for high accuracy and reliability.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document presents a comparative study of two segmentation methods - k-means clustering and fuzzy c-means clustering with genetic algorithm - for detecting brain tumors in MRI images. K-means clustering is used to segment MRI images into clusters and identify tumor regions. Fuzzy c-means clustering with genetic algorithm aims to improve upon k-means by eliminating over-segmentation issues and providing faster, more efficient clustering results. The experimental results indicate fuzzy c-means performs better than k-means for brain tumor segmentation. The document also reviews several other related works applying techniques like edge detection and probabilistic neural networks to segment brain tumors from MRI scans.
A novel framework for efficient identification of brain cancer region from br...IJECEIAES
Diagnosis of brain cancer using existing imaging techniques, e.g., Magnetic Resonance Imaging (MRI) is shrouded with various degrees of challenges. At present, there are very few significant research models focusing on introducing some novel and unique solutions towards such problems of detection. Moreover, existing techniques are found to have lesser accuracy as compared to other detection schemes. Therefore, the proposed paper presents a framework that introduces a series of simple and computationally cost-effective techniques that have assisted in leveraging the accuracy level to a very higher degree. The proposed framework takes the input image and subjects it to non-conventional segmentation mechanism followed by optimizing the performance using directed acyclic graph, Bayesian Network, and neural network. The study outcome of the proposed system shows the significantly higher degree of accuracy in detection performance as compared to frequently existing approaches.
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.
This document outlines a project on brain tumor detection and diagnosis using convolutional neural networks. It discusses the objective of outlining current automatic segmentation techniques using CNNs. It then provides an introduction on the importance of accurate brain tumor segmentation for diagnosis and treatment. The remaining sections cover literature reviews on CNN segmentation methods, the overall architecture and working principles, applications and the future scope of this area of research.
IRJET- Brain Tumor Detection using Hybrid Model of DCT DWT and ThresholdingIRJET Journal
The document presents a new hybrid model for detecting brain tumors in MRI images. It uses a combination of discrete cosine transform (DCT), discrete wavelet transform (DWT), principal component analysis (PCA), and fuzzy c-means clustering. DCT and DWT are applied to extract features from MRI images. PCA is then used to reduce the dimensions of the extracted features. Finally, fuzzy c-means clustering is used to segment and detect tumors. The proposed hybrid model is evaluated using objective metrics like RMSE, PSNR, correlation, contrast and entropy. Results show the hybrid model achieves better values for these metrics compared to using DCT or DWT alone, indicating it more accurately detects and segments tumors in MRI images.
Segmentation and Classification of Lung Nodule in Chest Radiograph ImageIJTET Journal
Abstract-Image segmentation plays a vital step in medical image processing. Lung cancer is the largest cause of tumor deaths. Since the nodules are commonly attached to blood vessels, detection of lung nodules is the challenging task .By early detection the lung cancer can be completely recovered. Especially in the case of lung nodule detection Computer Aided Detection (CAD) is effective for the improvement of radiologists‟ diagnosis. In this paper an efficient lung nodule detection scheme is developed by performing nodule segmentation through Fuzzy C-Means (FCM) and Virtual Dual Energy (VDE). Here the input image is considered as an radiograph image, then the lung is segmented by using Multi segment Active Shape Model (MASM). Finally neural network classifies as a nodule or non-nodule candidates.
SEGMENTATION OF MAGNETIC RESONANCE BRAIN TUMOR USING INTEGRATED FUZZY K-MEANS...ijcsit
Segmentation is a process of partitioning the image into several objects. It plays a vital role in many fields
such as satellite, remote sensing, object identification, face tracking and most importantly in medical field.
In radiology, magnetic resonance imaging (MRI) is used to investigate the human body processes and
functions of organisms. In hospitals, this technique has been using widely for medical diagnosis, to find the
disease stage and follow-up without exposure to ionizing radiation.Here in this paper, we proposed a novel
MR brain image segmentation method for detecting the tumor and finding the tumor area with improved
performance over conventional segmentation techniques such as fuzzy c means (FCM), K-means and even
that of manual segmentation in terms of precision time and accuracy. Simulation performance shows that
the proposed scheme has performed superior to the existing segmentation methods.
The IoT and registration of MRI brain diagnosis based on genetic algorithm an...IJEECSIAES
The technology of the multimodal brain image registration is the key method for accurate and rapid diagnosis and treatment of brain diseases. For achieving high-resolution image registration, a fast sub pixel registration algorithm is used based on single-step discrete wavelet transform (DWT) combined with phase convolution neural network (CNN) to classify the registration of brain tumors. In this work apply the genetic algorithm and CNN clasifcation in registration of magnetic resonance imaging (MRI) image. This approach follows eight steps, reading the source of MRI brain image and loading the reference image, enhencment all MRI images by bilateral filter, transforming DWT image by applying the DWT2, evaluating (fitness function) each MRI image by using entropy, applying the genetic algorithm, by selecting the two images based on rollout wheel and crossover of the two images, the CNN classify the result of subtraction to normal or abnormal, “in the eighth one,” the Arduino and global system for mobile (GSM) 8080 are applied to send the message to patient. The proposed model is tested on MRI Medical City Hospital in Baghdad database consist 550 normal and 350 abnormal and split to 80% training and 20 testing, the proposed model result achieves the 98.8% accuracy.
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
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8. 22760.pdf
1. TELKOMNIKA Telecommunication Computing Electronics and Control
Vol. 20, No. 4, August 2022, pp. 762~771
ISSN: 1693-6930, DOI: 10.12928/TELKOMNIKA.v20i4.22760 762
Journal homepage: http://telkomnika.uad.ac.id
Automated brain tumor detection of MRI image based on
hybrid image processing techniques
Lina A. Salman, Ashwaq T. Hashim, Ahmed M. Hasan
Control and Systems Engineering Department, Faculty of Engineering, University of Technology, Baghdad, Iraq
Article Info ABSTRACT
Article history:
Received Jan 05, 2022
Revised Jun 07,2022
Accepted Jun 15, 2022
Primary challenges are the identification, segmentation, and extraction of the
afflicted area from the scanning of magnetic resonance. However, it is a
time-consuming and tiresome for clinical specialists. In this paper,
an automated brain tumor system is proposed. The proposed system employs
hybrid image processing techniques such as contrast correction, histogram
normalization, thresholding techniques, arithmetic, and morphological
operations to quarantine nearby organs and other tissue from the brain for
improving the localization of the affected region. At first, the skull stripping
process is proposed to segregate the non-designated regions to extract the
designated brain regions. Those resultant brain region images are further
subjected to discover the brain tumor. The planned scheme is studied on the
magnetic resonance (MR) images with the use of T1, T2, T1c, and
fluid-attenuated inversion recovery (FLAIR). The proposed hybrid method
employed. The results reveal that the proposed method is quite efficient to
extract the tumor region. The accuracy rate for segmentation and separation
of area of interest in brain tumor reached to 95%. Finally, the significance of
the proposed procedure is confirmed using the real image clinical dataset got
from ten patients were diagnosed as begin, malignant, and metastatic brain
tumors in Al-Yarmouk and Baghdad teaching hospital in Baghdad, Iraq.
Keywords:
Arithmetic operations
Brain MRI
Contrast correction
Morphological operations
Skull stripping
Tumor
This is an open access article under the CC BY-SA license.
Corresponding Author:
Lina A. Salman
Control and Systems Engineering Department, Faculty of Engineering
University of Technology, Baghdad, Iraq
Email: cse.20.21@grad.uotechnology.edu.iq
1. INTRODUCTION
The rising technology in medical image processing has piqued interest in brain tumors and their
investigation. Conferring to a review prepared by the National Brain Tumor Foundation the realization of
brain tumors among the public, as well as the death rate from brain tumors, is outpacing previous year’s
numbers globally [1], [2]. In addition, various publications published in the last few decades have offered
contexts or ways to focus the brain tumor area, which might or might not be surveyed by phases such as
categorization, management planning, and prognosis of tumor. Segmentation of brain tumor medical pictures
is crucial, yet it is often hampered by issues such as inadequate contrast, noise, and missing boundaries.
Diagnostic procedures such as computation tomography scan, positron emission tomography and magnetic
resonance imaging, are used to effectively control these parameters [3]. These imaging techniques aid in the
detection of a variety of disorders. magnetic resonance imaging is popular in successfully identifying and
treating brain cancers since they use innocuous magnetic fields and radio waves [4].
Malignancy progresses in the body when a cell’s growth and division become abandoned. It is
called a brain tumor because of its location in the brain. A brain tumor is a mass of unneeded and aberrant
2. TELKOMNIKA Telecommun Comput El Control
Automated brain tumor detection of MRI image based on … (Lina A. Salman)
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cell development in the brain or an encephalon lesion takes up space inside the skull and causes a surge in
intracranial pressure [5]. Because of differences in the size, site, and intensities, segmenting of brain tumors
in magnetic resonance imaging is a challenging task [6], [7]. Segmentation of the image is a crucial stage in
using magnetic resonance images of brain cancer research where the segmented part of the brain tumor might
ignore perplexing features from another brain area, consenting for a more precise diagnosis of brain tumor
subtypes and informing later diagnosis [8]. Brain tumor growth, shrinking, and relapse, may all be tracked
using segmentation of linear magnetic resonance images (MRI) data. In modern clinical practice, manual
delineation by operators is still employed for segmentation [9]. Manual the segmentation is a timewasting job
that often includes segment-by-segment methods, and the results are highly reliant on the operators’ skulls
and knowledge. Furthermore, exact results are difficult to acquire even with the same operator. A multimodal
and longitudinal clinical experimental causes a fully programmed, objective, and repetition of the
segmentation method [10].
Radiation management for brain tumors relies on proper (MRI) segmentation, which necessitates the
precise pixel labeling of MRI scans as healthy tissue or tumor [11]. However, Figure 1(a) T2-w (T2-weighted),
Figure 1(b) T1-w (T1-weighted), Figure 1(c) fluid-attenuated inversion recovery (FLAIR), and Figure 1(d)
T1c-w (T1-weighted) magnetic resonance images modalities are employed in brain tumor splitting up and
abstraction. This is because of T1c-w is counterbalance-enhanced, and FLAIR is a fluid-attenuated reversal,
as shown in Figure 1. Glial cell tumor, for example, has fuzzy boundaries that make it difficult to
differentiate between it and other normal tissue [12].
(a) (b) (c) (d)
Figure 1. Samples of four pathological MRI slices: (a) T2-w, (b) T1-w, (c) FLAIR, and (d) T1c-w [11]
2. RELATED WORK
Brain tumor segmentation for diagnosis is a hard process. Segmentation refers to the partition of an
image into different relevant segments. The availability of publicly available datasets has recently given a
common venue for academics to create and objectively evaluate their methods using existing techniques.
Mehena and Adhikary [13] demonstrated an upgrade to the watershed transform for segmentation
and morphological operator-based brain tumor retrieval from magnetic resonance (MR) images in 2015.
When contrast enhancement is used, this technique can extract brain tumors from MR images in a variety of
age groups. The proposed technique provides more information about brain tumors and aids physicians in
diagnosing them. Unfortunately, a significant disadvantage of this strategy is excessive segmentation and
poor detection of signals with a low signal-to- noise ratio.
Dhanve and Kumar [14] proposed an efficient picture segmentation strategy based on the technique
named K-means clustering, which is combined with the s algorithm of fuzzy C-mean in 2015. As a
consequence, to ensure the brain tumors detection is successfully achieved, stages of thresholding and level set
segmentation were carried out. The suggested technique capitalizes on the advantages of K-means clustering for
picture segmentation to compute time. It can benefit from the accuracy advantages of fuzzy C-mean s.
The K-mean approach is faster than fuzzy C-mean s in detecting brain tumors; yet, fuzzy C-mean s is more
accurate at predicting tumor cells; however, this method is not suited for non-convex shape data segmentation.
In 2015, Pan et al. [15] applied an enhanced convolutional neural network and non-quantifiable
local surface feature with a structure of deep learning conventional approach for brain tumor detection.
In which three-layer conventional neural network and multiple phase magnetic resonance imaging level was
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used to improve the performance rate. The experimental result shows improvement in sensitivity, specificity
and performance of about 18% over the bassline one but, the disadvantage of this method is it is complex and
may not apply in the simple conventional approach and the date which used for low grade is relatively small.
In 2016, Bhima and Jagan [16] proposed Watershed techniques for brain MRI images. Their
technique was involving on region detection and mathematical morphology, which made this technique
useful for grayscale image segmentation. Their work investigates the existing techniques for segmentation of
brain image, such as the using both of K-means and fuzzy C-mean s, multi-region and multi-reference
frameworks and fuzzy knowledge-based seeded region growing (FKSRG).
In 2017, Bahadure et al. [17] presented strategies of berkeley wavelet transformation )BWT(
imaging for identification of brain tumor and using magnetic resonance imaging. By using skull stripping,
95% segmentation was achieved. this was carried out by omitting all non-brain tissues for identification
reasons. However, this technique requires a lengthy training period for huge datasets.
Chen et al. [18] published a method for segmenting MRI images in 2017 that incorporates fuzzy
clustering and markov random fields (MRF). MAP-MRF is used to merge the coarse scale image and the
membership of fuzzy clustering extracted from the original image into potential functions of the single-site
clique. To model the neighborhood constraint with MRF, defined potential functions and distance weights are
introduced. The proposed method has an average similarity index of 36.8%, 33.7%, and 2.75% higher than
fuzzy C-mean (FCM), fast generalized fuzzy C-mean (FGFCM), an fuzzy local information C-means
clustering algorithm (FLICM), respectively, showing that the MRI segmentation method is a robust and
precise method. However, the certainty of the probability of some value is one method’ drawbacks.
In 2018, Devkota et al. [19] proposed using mathematical morphological reconstruction (MMR) to
develop a computer-aided detection method for the early stages of brain tumors diagnosing. Their method
involved in preprocessing the image to eliminate artifacts and noise before the image is being segmented in
order to identify regions with possible tumor. The results show a high level of accuracy and significantly
lowering time of calculation. Their work shows that their technique may be successfully used in diagnosing
of brain tumors in patients. However, the computational complexity acts as a barrier to this strategy.
In 2018, Shree and Kumar [20] demonstrated how to extract seven characteristics from GLCM
pictures using region-based segmentation. MRI Images with a resolution of 256×256, 512×512 pixels are
included in the dataset. The dataset was compiled from the website www.diacom.com. 95% of testing datasets
are accurate. One disadvantage is that a seed point with a different value may produce a different value.
In 2019, Mascarenhas et al. [21] introduced a histogram normalization, contrast correction and
binarization strategy for decoupling nearby structures from the brain. Besides, they enhancing the region of
interest of brain tumors. The results were compared to manual segmentations performed by two radiologists
to ascertain out the algorithms’ efficacy. The poor results showed that the suggested system could
autonomously locate and delineating the tumor location without user intervention, based on two novel
strategies for detecting brain extreme sites on magnetic resonance imaging.
In 2019, Kumar and Kumar [22] reported segmentation and feature extraction using the FCM
clustering algorithm and gray-level cooccurrence matrix (GLCM) and Gabor are nine features. Preprocessing
is accomplished using a median filter. The precision is 91.17%.
Jeong et al. [23] suggested in 2020 to use a reconstructing convolutional neural network 3D mask
region-based convolutional neural network (R-CNN) approach. However, their method detects and segments
a high and low grade of the brain cancers automatically for dynamic susceptibility contrast MRI images. This
study enrolled 22 high and low-grade patients with 50 to 70 of perfusion time point volumes. The proposed
technique achieved an overall 895 of dice similarity, 90% of precision, 87% of recall, and 1.270.67% of
center of a mass distance. This precision is a result of a lack of specificity.
In 2021, Sumir et al. [24] proposed a fully automatic brain tumor segmentation approach with
updated morphological and thresholding operations. This approach was used for segmenting uncontrolled
growth of mass-containing tissues from brain tumor MRI images. Moreover, this approach achieved more
accuracy results, and it reduced computational time. Additionally, and based on the quantity of area, this
approach aids in determining the stage of tumor efficiently. By using the approach of discrete wavelet
transform (DWT) for the MRI image, most of the mean, correlation, contrast, skewness, energy and
homogeneity are determined.
In 2021, Anantharajan and Gunasekaran [25] used a weighted fuzzy factor approach based on
kernel metrics. To improve prediction accuracy, a deep autoencoder (DAE) combined with a weighted fuzzy
clustering algorithm was applied in order to provide a segmentation for the lesion area from the remaining
parts of MRI image. The planned procedure affords better performance with accurateness related to the
further current procedures.
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3. PROPOSED METHOD
With the aid of non-specialists, this study proposed approach for understanding of the diagnostic
and decide whether the tumor is existed or not in the area of interest. To build and verify a suite of
computational algorithms for automatic tumor segmentation on magnetic resonance imaging of the brain.
Pre-processing, skull stripping, and tumor segmentation are the three phases that make up the approach.
The suggested system steps are depicted in Figure 2.
3.1. Pre-processing
Pre-processing includes techniques for removing artifacts from photographs, enhancing their quality.
And for more segmentation precision and comprehensive visualization, highlighting an area of interest is
included as well. To highlight the tumor location, this stage involved picture contrast enhancement. In all
photos, the intensities of grey-level pixel were uniform between zero and one. Equality of histograms unlike
other imaging techniques, MRI does not have a contract value for the pixel intensity concerning the tissue
image, i.e., the varied intensities might exist in the same tissue, which make it easy to use the properties of
intensities as a useful information during segmenting pictures. The non-standardized intensities problems can
be reduced by using the method of normalizing. Histogram equalization is used to make the anticipated
portions of the image brighter than the surrounding area, making extraction easier.
Figure 2. The proposed system of brain tumor detection of MRI
3.2. Skull stripping
This step is suggested to automatically externally eliminate the areas which are not useful, such as
the meninges, the skull bone, and subcutaneous fat, by used gamma correction, thresholding, arithmetic
operation, and morphological operations. The skull stripping procedure is a necessary pre-processing step
that separates non-specified brain regions to extract the designated brain regions. The resulting brain region
images are then analyzed to determine a brain tumor. Algorithm 1 illustrates the stages involved in skull
stripping, whereas Figure 3 illustrates the steps involved in skull stripping. Figure 3(a) original image,
Figure 3(b) correct gamma, Figure 3(c) binarization, Figure 3(d) image complement, Figure 3(e) great mask,
Figure 3(f) image multiplications, and Figure 3(g) skull stripping.
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(a) (b) (c) (d)
(e) (f) (g)
Figure 3. Steps of skull stripping: (a) original image, (b) correct gamma, (c) binarization, (d) image
complement, (e) great mask, (f) image multiplications, and (g) skull stripping
Algorithm 1. Skull stripping
Input: BrainImg height, width
Output: Skull
Step 1: Apply inverse of gamma transform with 𝛾 = 2.5
𝑁𝑒𝑤𝑖𝑚𝑔 = 255 × (255/𝐵𝑟𝑎𝑖𝑛𝐼𝑚𝑔) ^ (1/𝛾) (1)
Step 2: The binarization creates a binary image using a threshold got using Otsu’s method
BinImg = imbinarize (Newimg, TH)
Step 3: Perform image complement to BinImg
CompImage = imcomplement (BinImg);
Step 4: Create mask
Step 4.1: Perform image closing and opening on CompImage
Step 4.2: Mask = imcomplement (CompImage)
Step 5: Perform image multiplication between the mask and original image BrainImg
Skull = immultiply (mask, BrainImg)
Step 6: Skull with a white background
for i = 1 to width
for j = 1: height
if ((Mask (i, j) <> 0))
Skull2 (i, j) = BrainImg (i, j);
else
Skul2 (i, j) =255;
endif
end for j
end for i
3.3. Tumor segmentation
The segmentation method is incredibly significant in image processing. The findings of
segmentation will be utilized to extract quantitative information from images, such as grouping and
thresholding [26]. The suggested method used enhanced techniques to highlight the tumor region of interest
(ROI) (i.e., tumor region), while morphological operators are used to remove unwanted features and recreate
the tumor’s shape and texture. The steps of tumor segmentation are listed in Algorithm 2.
Figure 4 shows the results provided by algorithm 2 which is drawn from the extracted tumor region
and segmented outcome. Figure 4(a) original image, Figure 4(b) after skull stripping, Figure 4(c) correct
gamma, Figure 4(d) after filling and addition, Figure 4(e) binarization, Figure 4(f) morphological operations,
Figure 4(g) segment tumor region, and Figure 4(h) boundary of tumor in original image.
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Algorithm 2. Tumor segmentation
Input: MI // Brain image
Height, width
Output: ROI: localized ROI region
Step 1: Read brain image MI of size W×H
Step 2: Apply algorithm 1 for skull stripping and return brain image brain
Step 3: Apply contrast stretching to increase Brain intensity values using the (2)
𝐸𝑏𝑟𝑎𝑖𝑛(𝑥, 𝑦)={
0 𝐵𝑟𝑎𝑖𝑛(𝑥, 𝑦) ≤ 𝐿𝑜𝑤
255 ×
𝐵𝑟𝑎𝑖𝑛(𝑥,𝑦)−𝐿𝑜𝑤
𝐻𝑖𝑔ℎ−𝐿𝑜𝑤
𝐿𝑜𝑤 < 𝐵𝑟𝑎𝑖𝑛 (𝑥, 𝑦) < 𝐻𝑖𝑔ℎ
255 𝐵𝑟𝑎𝑖𝑛 (𝑥, 𝑦) ≥ 𝐻𝑖𝑔ℎ
(2)
Where Ebrain (𝑥, 𝑦) is the enhanced image and brain (𝑥, 𝑦) is the brain image.
Low and high levels are chosen through trial and error.
Step 4: The enhanced brain image Ebrain is recorded using Gamma correction demarcated by the (3)
𝐺𝑏𝑟𝑎𝑖𝑛 = ( (𝐸𝑏𝑟𝑎𝑖𝑛/255)^ 𝛼) × 255 (3)
Where Gbrain is a gamma image, Ebrain for medical image (MI), and α gamma factor for gamma factor.
When the value is between 0 and 1, it improves contrast in bright areas, and when it is greater than 1,
it improves contrast in dark areas.
The number of 2.5 was chosen to brighten the medical image and make the ROI region more distinct.
For the greatest results in this phase, choose this value.
Step 5: Fill gaps and apply image addition between the addition Gbrain after region filling and Ebrain to
highlight tumor region
Step 6: Apply global thresholding to segment the tumor image
Step 7: Apply morphological operation. For a binary image, use the morphological closure operator
The closing filter operation smoothes the edges, reduces minor inner collisions, ties narrow joints, and fills
small gaps caused by noise.
Step 8: segment the tumor region
Completes tumor segmentation by evaluating each spatially split area in image space discretely. Area free of
the lesion are unconcerned. with those regions remains labeled as a tumor. The subsequent image is
considered the last tumor segmentation.
Step 9: Contour boundary of tumor in the original image
(a) (b) (c) (d)
(e) (f) (g) (h)
Figure 4. Steps of tumor segmentation: (a) original image, (b) after skull stripping, (c) correct gamma,
(d) after filling and addition, (e) binarization, (f) morphological operations, (g) segment tumor region and
(h) boundary of tumor in original image
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4. RESULTS AND DISCUSSION
The suggested method is evaluated on two datasets: one is publicly available [27] and the second is
a private dataset. The private dataset was compiled from 10 patients who are diagnosed as begin, malignant
and metastatic brain tumors in Al-Yarmouk and Baghdad teaching hospitals in Baghdad-Iraq. All patients
that enter study are between the ages of 18 and 60. Their MRI scans were saved in a database in a photo in
the JPG, bmp, JFIF, and JPEG formats. The total number of tumor images for each MRI sequence
investigated in this paper is 250 tumor images with T1, T2, T1c, and FLAIR modalities. The white color
represents the suspicious area of the MRI image. This area of the image has the maximum intensity compared
to the rest of the image. In this section, we looked at two different Brain MRI datasets for brain tumor
segmentation. Figure 5 depicts some samples of the results for skull stripping. The results of skull stripping are
depicted in Figure 5. The tumor segmentation testing results exhibit superior results, as illustrated in Figure 6
and Figure 7.
Figure 5. Samples of skull stripping process
Figure 6. Samples of segmentation of brain tumours lesion from Kaggale
8. TELKOMNIKA Telecommun Comput El Control
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The algorithm proposed in this paper can successfully extract brain tumors with a 95% accuracy in
various age groups, as calculated by the equation below. The accuracy of hybrid segmentation was compared
to that of fuzzy and C-mean, fuzzy and K-mean, improved fuzzy and watershed algorithm, contrast
correction, and intelligent mean shift, which were 85%, 86%, 88%, 89%, and 92% as shown in Table 1.
We present the MATLAB2019b application of this context to validate the visual benefits of hybrid image
processing approaches algorithm for localizing brain tumor in MRI images to show the results and theoretical
construction proposed in this study. MATLAB is a widely used multifunctional numeric programming
language that carried out higher-order mathematical calculations.
Figure 7. Samples of tumor segmentation for real patient Images
Accuracy is the measure of successful segmentation of the proposed algorithm has been calculated by the (4).
𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦(%) =
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑜𝑟𝑟𝑒𝑐𝑡 𝑑𝑎𝑡𝑎
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑎𝑙𝑙 𝑑𝑎𝑡𝑎
× 100 (4)
Table 1. Compression accuracy rate between proposed methods and other methods
Authors Technique Accuracy rate (%)
[28] Intelligent mean shfit 92
[20] Contrast corection 89
[29] Improved fuzzy and watershed 88
[30] Fuzzy and K-mean 86
[31] Fuzzy and C- mean 85
Proposed method Hybrid image processing technique 95
5. CONCLUSION
MRI images are commonly used in the diagnosis of brain benign and malignant brain tumor.
The current study used advance modality which is of hybrid image processing techniques for segmenting of
brain tumors from MRI brain images to minimizing the effect of artifact and for separation and segmentation
of the suspicious area at the same time. These hybrid image processing approaches and procedures give
excellent brain tumor segmentation outcomes with accuracy reaching 95% which is given the novality for
this work in compare to previous studies, according to the results gathered. Pre-processing procedures could
successfully remove enormous parts of the brain and some cerebrum areas. The initial step in the brain
picture segmentation procedure is stripping off the skull. Cranium stripping is mandatory to remove the bone
and the surrounding zone from the MRI. It is necessary for a thorough investigation of a brain tumor using
MR imaging. Morphological procedures are primarily used as the filter in the proposed method to eliminate
low-frequency pixels and border pixel tumor location.
9. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 20, No. 4, August 2022: 762-771
770
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BIOGRAPHIES OF AUTHORS
Lina A. Salman has graduated from Computer Engineering Department at
AL-Mustansiriyah University, she works as at the University of Technology, she is currently a
master student at Control and Systems Engineering Department, University of Technology- Iraq.
She is intertested in Image processing, Security, and Intelligent Systems. She can be contacted at
email: cse.20.21@grad.uotechnology.edu.iq.
Ashwaq T. Hashim has graduated from Computer Science Department at the
Baghdad University. She obtained M.Sc. from Computer Science, University of Basrah in
2003. She worked as Assistant Lecturer in the Control and Systems engineering department
from 2003 to 2006. She received her scientific promotion to be a university Lecturer in 2006.
And she received her scientific promotion to be an assistant professor in 2009. At 2014 she
received a PhD degree from Babylon university-Iraq, she had published more than 45 papers
mostly in the field of image processing and security, she received her scientific promotion to
be a professor in 2019. She can be contacted at email: Ashwaq.T.Hashim@uotechnology.edu.iq.
Ahmed M. Hasan he received the B.Sc. degree in Control and Systems
Engineering (Computer Branch) from the Control and Systems Engineering Department 2002,
University of Technology, the M.Sc. degree in Computer Engineering from the same department
2006, the Ph.D. degree in Computer Engineering from the Universiti Putra Malaysia, Malaysia.
Currently, I’m working on Hybrid Intelligent Systems with optimization techniques. He can be
contacted at email: 60163@uotechnology.edu.iq.