Scholarlink

Brain cancer is a severe health issue that necessitates ongoing innovation in detection and treatment. One of the most important components of brain cancer analysis is identifying the brain cancer location and classifying the brain tumour type using magnetic resonance imaging (MRI) data. This is a difficult undertaking due to the diversity, complexity and heterogeneity of brain tumours. Based on the most recent improvements in feature extraction techniques, we offer a novel framework for efficiently identifying brain cancer regions and classifying brain cancers from brain magnetic resonance imaging. Feature extraction is the technique of extracting useful information from visual data to help in brain tumour diagnosis, classification and prognosis. Our approach includes four major steps: (a) image preprocessing, which includes image intensity normalisation and data augmentation; (b) image segmentation, which divides the image into regions of interest; (c) feature extraction, which uses deep convolutional neural networks (CNNs) to compute the most distinctive deep features from the segmented regions; and (d) feature fusion and classification, which combines the deep features from multiple CNNs and uses machine learning classifiers to determine the type. We provide an overview of the many imaging modalities and data sources employed, which range from medical pictures like Magnetic Resonance Imaging and computed tomography scans to genetic and promote information. We also examine and contrast the merits and disadvantages of several feature extraction approaches, including classic handmade features, machine learning-based techniques and sophisticated deep learning methods. We cover the future prospects and problems of this rapidly expanding subject, such as the need for vast and diverse datasets, multimodal data integration, and model building that is explainable and resilient. The goal of this study is to help academics and clinicians interested in the function of feature extraction in brain cancer analysis go further by summarising the available literature and offering a useful reference.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Novel Approach for Identifying Brain Cancer Regions and Classification from Brain MRI Images

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Tiyas Sarkar,
  • Manik Rakhra

摘要

Brain cancer is a severe health issue that necessitates ongoing innovation in detection and treatment. One of the most important components of brain cancer analysis is identifying the brain cancer location and classifying the brain tumour type using magnetic resonance imaging (MRI) data. This is a difficult undertaking due to the diversity, complexity and heterogeneity of brain tumours. Based on the most recent improvements in feature extraction techniques, we offer a novel framework for efficiently identifying brain cancer regions and classifying brain cancers from brain magnetic resonance imaging. Feature extraction is the technique of extracting useful information from visual data to help in brain tumour diagnosis, classification and prognosis. Our approach includes four major steps: (a) image preprocessing, which includes image intensity normalisation and data augmentation; (b) image segmentation, which divides the image into regions of interest; (c) feature extraction, which uses deep convolutional neural networks (CNNs) to compute the most distinctive deep features from the segmented regions; and (d) feature fusion and classification, which combines the deep features from multiple CNNs and uses machine learning classifiers to determine the type. We provide an overview of the many imaging modalities and data sources employed, which range from medical pictures like Magnetic Resonance Imaging and computed tomography scans to genetic and promote information. We also examine and contrast the merits and disadvantages of several feature extraction approaches, including classic handmade features, machine learning-based techniques and sophisticated deep learning methods. We cover the future prospects and problems of this rapidly expanding subject, such as the need for vast and diverse datasets, multimodal data integration, and model building that is explainable and resilient. The goal of this study is to help academics and clinicians interested in the function of feature extraction in brain cancer analysis go further by summarising the available literature and offering a useful reference.