Paper Title
A Review on Brain Disease Diagnosis Using Deep Learning
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
Author - Abhishek Sahu, Purvi Tiwari, Sachin Harne
Published : Volume-10,Issue-5 ( May, 2023 )
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Published on 2023-09-26 |
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