DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Electronics and Computer Science-IJAECS |
 Journal Home Volume Issue |
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Issue |
Volume-10,Issue-5 ( May, 2023 ) | |||||||||
Paper Title |
A Review on Brain Disease Diagnosis Using Deep Learning | |||||||||
Author Name |
Abhishek Sahu, Purvi Tiwari, Sachin Harne | |||||||||
Affilition |
1Research Scholar, Department of computer Science and Engineering, RSR Rungta College of Engineering and Technology, Bhilai, Chhattisgarh, India 2,3Assistant Professor, Department of computer Science and Engineering, RSR Rungta College of Engineering and Technology, Bhilai, Chhattisgarh, India | |||||||||
Pages |
74-78 | |||||||||
Abstract |
Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area. Keywords - Deep Learning, Bioinformatics, Segmentation, Medical Images, Tumor | |||||||||
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