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Brain Tumor segmentation is one of the most crucial and arduous tasks in the field of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it becomes a tedious task when there is a large amount of data present to be processed manually. Brain tumors have diversified appearanc...
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The segmentation of a brain tumor is an exciting and exigent research task in the field of medical image analysis. An early finding of a brain tumor aids to obtain effective treatment and boosting the survival time of the patients. The brain tumor segmentation segregates the abnormal tissues region from the normal tissues region. The major challeng...
Brain Tumor segmentation is one of the most crucial and arduous tasks in the terrain of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it is an aggravating task when there is a large amount of data present to be assisted. Brain tumors have high diversity in appearance...
Citations
... The faster R-CNN could achieve 91.66% accuracy. Hossain et al. [10] used a Fuzzy C-Means clustering algorithm to extract features. There were six classifiers used in this method. ...
Brain tumor refers to the formation of abnormal cells in the brain. It can be divided into benign and malignant. The main diagnostic methods for brain tumors are plain X-ray film, Magnetic resonance imaging (MRI), and so on. However, these artificial diagnosis methods are easily affected by external factors. Scholars have made such impressive progress in brain tumors classification by using convolutional neural network (CNN). However, there are still some problems: (i) There are many parameters in CNN, which require much calculation. (ii) The brain tumor data sets are relatively small, which may lead to the overfitting problem in CNN. In this paper, our team proposes a novel model (RBEBT) for the automatic classification of brain tumors. We use fine-tuned ResNet18 to extract the features of brain tumor images. The RBEBT is different from the traditional CNN models in that the randomized neural network (RNN) is selected as the classifier. Meanwhile, our team selects the bat algorithm (BA) to optimize the parameters of RNN. We use five-fold cross-validation to verify the superiority of the RBEBT. The accuracy (ACC), specificity (SPE), precision (PRE), sensitivity (SEN), and F1-score (F1) are 99.00%, 95.00%, 99.00%, 100.00%, and 100.00%. The classification performance of the RBEBT is greater than 95%, which can prove that the RBEBT is an effective model to classify brain tumors.
... Year Technique Chen et al. [73] 1998 Atlas D. Comaniciu and P. Meer [74] 2002 Deformable Model Selvathi et al. [75] 2005 FCM Li et al. [76] 2005 Deformable Model Akselrod-Ballin et al. [77] 2006 Atlas Kong et al. [78] 2007 FCM He et al. [79] 2007 Deformable Model Kuo et al. [80] 2008 Hybrid Khotanlou et al. [81] 2009 FCM Kannan et al. [82] 2009 FCM Ping-Feng Chen [83] 2009 Deformable Model Karsch et al. [84] 2009 Hybrid Chandra et al. [85] 2009 SVM Khotanlou [81] 2009 Hybrid Yamamoto.D et al. [86] 2010 Hybrid Mishra et al. [87] 2010 Neural Network Shasidhar et al. [88] 2011 FCM Noreen et al. [89] 2011 FCM Zhang et al. [90] 2011 Deformable Model Bhattacharyya et al. [91] 2011 Hybrid Koley et al. [92] 2011 ROI based M. Jafari and S. Kasaei [93] 2011 Hybrid A.S.Bhide et al. [94] 2012 FCM Jean-Philippe et al. [95] 2012 Atlas Sergio Pereira et al. [96] 2016 Neural Network M.Reza et al. [99] 2017 Super Pixel M.Soltaninejad et al. [100] 2017 Neural Network Christoph Baur et al. [102] 2018 Neural Network A Kader Isselmou et al. [103] 2019 Neural Network Zhenyu Tang et al. [104] 2019 Neural Network P. Mohamed Shakeel et al. [105] 2019 Neural Network P. Kumar Mallick et al. [106] 2019 Neural Network Changhee Han et al. [107] 2019 Neural Network M. Li et al. [108] 2019 Neural Network Tonmoy Hossain et al. [109] 2019 Neural Network Sultan Noman Qasem et al. [110] 2019 Neural Network Javaria Amin et al. [111] 2019 LSTM Md Shahariar Alam et al. [112] 2019 FCM Janardhanaprabhu and Malathi [113] 2019 Depth-First Search(DFS) Rajan,P.G and Sundar, C [114] 2019 KM-FCM M. Mohammed Thaha [115] 2019 Neural Network N. Noreen et al. [116] 2020 Neural Network Muhammad Sharif [117] 2020 Extreme Learning Priyansh Saxena [118] 2020 Neural Network ...
... Hossain et al. [109] developed the method to segment the brain tumor from MRI images using FCM and CNN as a classifier. Compared to other traditional classifiers, their method worked well on the CNN classifier with an accuracy of 97.87%. ...
Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain disease and monitor treatment as non-invasive imaging technology. MRI produces three-dimensional images that help neurologists to identify anomalies from brain images precisely. However, this is a time-consuming and labor-intensive process. The improvement in machine learning and efficient computation provides a computer-aid solution to analyze MRI images and identify the abnormality quickly and accurately. Image segmentation has become a hot and research-oriented area in the medical image analysis community. The computer-aid system for brain abnormalities identification provides the possibility for quickly classifying the disease for early treatment. This article presents a review of the research papers (from 1998 to 2020) on brain tumors segmentation from MRI images. We examined the core segmentation algorithms of each research paper in detail. This article provides readers with a complete overview of the topic and new dimensions of how numerous machine learning and image segmentation approaches are applied to identify brain tumors. By comparing the state-of-the-art and new cutting-edge methods, the deep learning methods are more effective for the segmentation of the tumor from MRI images of the brain.
... The other six traditional methods, SVM, KNN, logistic regression, naive Bayes, random forest classifier, and multilayer perceptron, were also applied with scikit-learn for verification of results. Still, the convolutional neural network shows better results than the traditional methods [42]. ...
Brain tumor is one of the most dreadful worldwide types of cancer and affects people leading to death. Magnetic resonance imaging methods capture skull images that contain healthy and affected tissue. Radiologists checked the affected tissue in the slice-by-slice manner, which was time-consuming and hectic task. Therefore, auto segmentation of the affected part is needed to facilitate radiologists. Therefore, we have considered a hybrid model that inherits the convolutional neural network (CNN) properties to the support vector machine (SVM) for the auto-segmented brain tumor region. The CNN model is initially used to detect brain tumors, while SVM is integrated to segment the tumor region correctly. The proposed method was evaluated on a publicly available BraTS2020 dataset. The statistical parameters used in this work for the mathematical measures are precision, accuracy, specificity, sensitivity, and dice coefficient. Overall, our method achieved an accuracy value of 0.98, which is most prominent than existing techniques. Moreover, the proposed approach is more suitable for medical experts to diagnose the early stages of the brain tumor.
The 'SwiftApply Assistant' addresses the common challenge of time consuming data entry across a multitude of applications. This innovative tool simplifies interactions with forms such as job applications, admissions, claims, scholarships, and healthcare enrollment by automating the process of filling HTML forms. Noteworthy features include its adaptability to various life stages and a user centric design aimed at streamlining the application process while offering control and customization options. With cross platform accessibility ensuring convenience and scalability to adapt to emerging application types, users can expect a significant reduction in time and effort by eliminating the need for repetitive data entry. Key Words: Block chain, Medical Report, Data Accessibility, Security, Encryption, Data Retrieval.
Brain tumor detection via machine learning, specifically with MRI scans, is a significant advancement in medical imaging. The method aims to enhance diagnostic accuracy for brain neoplasms, aiding in early detection and precise classification. It involves preprocessing MRI data and employing convolutional neural networks (CNNs) to extract complex features. Training, validation, and testing utilize a large annotated dataset encompassing various tumor types and stages. Key steps include data collection, preprocessing, and feature extraction. Key Words: Brain tumor detection, Machine learning, MRI scans, Convolutional Neural Networks, Preprocessing, Annotated dataset.
Brain tumor segmentation has been a challenging and popular research problem in the area of medical imaging and computer-aided diagnosis. In the last few years, especially since 2017, researchers have significantly contributed for solving and enhancing the performance of brain tumor abnormality detection and tumor segmentation from magnetic resonance (MR) images. This paper presents a detailed and intensive review of automated brain disease diagnosis and tumor segmentation methods obtained by investigating numerous recent articles. In the first phase, an extensive literature search is conducted with more than 600 articles from medical image analysis, brain disease diagnosis, and tumor segmentation. Around 50% of articles are removed after initial scanning based on certain criteria, i.e., publication year, number of citations, and bibliographic indexing. A total of 161 relevant articles are finally selected in the second phase based on their performance and novelty of the proposed methods. Furthermore, the selected articles are investigated from the perspectives of methodology and performance. Overall methods exploited for brain disease detection and tumor segmentation are categorised into three broad classes, i.e., conventional methods, machine learning-based methods, and deep learning-based methods. As deep learning-based methods are state-of-the-art for computer-aided diagnosis (CAD) nowadays, we investigated several deep learning models, such as the convolutional neural network (CNN), the generative adversarial network (GAN), the U-Net, etc., along with residual block and attention gate, with respect to their learning mechanisms and hyper-parameter tuning. Methods from each class are rigorously reviewed and summarised by identifying their advantages, disadvantages, dataset, MR modality used, and type of images (2D/3D) processed. The methods are also analysed and compared based on their performance in various measures such as dice similarity coefficient (DSC), sensitivity, positive predictive value (PPV), Specificity, Jaccard Index (JI), Accuracy, Hausdorff distance, and computation time. In this review, the high heterogeneity of articles based on different methodologies is considered in light of the recent progress and development of brain tumor detection and segmentation. During analysis, it has been observed that deep learning-based methods, especially various variants of the U-Net model, outperform other approaches for brain tumor segmentation.
Cancer associated with the nervous system and brain tumors ranks among the leading causes of death in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) capture brain images. MRI is pivotal in diagnosing brain tumors and analyzing other brain disorders. Typically, radiologists or experts manually assess MRI images to detect brain tumors and abnormalities in the early stages for appropriate treatment. However, early brain tumor diagnosis is complex, necessitating computerized methods. This research introduces a novel approach for the automated segmentation of brain tumors and a framework for classifying brain tumor regions. The proposed methods comprise several stages: preprocessing, enhancing the coherence of MRI brain images using Contrast Limited Adaptive Histogram Equalization (CLAHE) and diffusion filtering in the first two steps, followed by the segmentation of the region of interest using the Fuzzy C-Means (FCM) clustering technique in the third step. The last step involves classification using the Support Vector Machine (SVM) classifier. The classifier is applied to different brain tumor types, from meningioma to pituitary tumors, utilizing the CE-MRI database. The proposed method exhibits significantly improved contrast and proves the effectiveness of the classification framework, achieving an average sensitivity of 0.977, specificity of 0.979, accuracy of 0.982, and a Dice score (DSC) of 0.961. Furthermore, this method demonstrates a shorter processing time of 0.42 seconds compared to existing approaches. The performance of this method underscores its significance when compared to state-of-the-art methods in terms of sensitivity, specificity, accuracy, and DSC. For future enhancements, it is possible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.
