Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods

• Published in: Journal of cancer research and clinical oncology 2023-12, Vol.149 (18), p.16293-16309
• Main Authors: Song, Guanghui, Xie, Guanbao, Nie, Yan, Majid, Mohammed Sh, Yavari, Iman
• Format: Article
• Language: English
• Subjects: brain; Brain cancer; Brain research; Brain tumors; Cancer Research; Classification; Deep learning; Diagnosis; Glioma; Hematology; Image processing; Internal Medicine; Magnetic resonance imaging; magnetism; Medicine; Medicine & Public Health; model validation; Neural networks; Neuroimaging; Oncology; Segmentation; systematic review
• ISSN: 0171-5216; 1432-1335
• DOI: 10.1007/s00432-023-05389-4

Purpose Convolutional Neural Networks (ConvNets) have quickly become popular machine learning techniques in recent years, particularly in the classification and segmentation of medical images . One of the most prevalent types of brain cancers is glioma, and early, accurate diagnosis is essential for both treatment and survival. In this study, MRI scans were examined utilizing deep learning techniques to examine glioma diagnosis studies. Methods In this systematic review, keywords were used to obtain English-language studies from the Arxiv, IEEE, Springer, ScienceDirect, and PubMed databases for the years 2010–2022. The material needed for review was then collected from the articles once they had been chosen based on the entry and exit criteria and in accordance with the research's goal. Results Finally, 77 different academic articles were chosen. According to a study of published articles, glioma brain tumors were discovered, categorized, and segmented utilizing a coordinated approach that included image collecting, pre-processing, model design and execution, and model output evaluation. The majority of investigations have used publicly accessible photo databases and already-trained algorithms. The bulk of studies have employed Dice's classification accuracy and similarity coefficient metrics to assess model performance. Conclusion The results of this study indicate that glioma segmentation has received more attention from researchers than glioma detection and classification. It is advised that more research be done in the areas of glioma detection and, particularly, grading in order to be included in systems that support medical diagnosis.