MEDICAL IMAGE ANALYSIS USING DEEP LEARNING: APPLICATIONS AND CHALLENGES

  • Sonam Chambers, Shailendra Kumar Shriwastava

Abstract

Deep learning has emerged as a powerful tool for medical image analysis, revolutionizing the field with its ability to extract intricate patterns and features from complex data. This technology has shown remarkable success in various applications, including disease diagnosis, lesion segmentation, and treatment planning. However, the adoption of deep learning in medical imaging also presents significant challenges, such as data scarcity, annotation costs, and the need for interpretability and robustness. This paper provides a comprehensive overview of the current applications of deep learning in medical image analysis, highlighting the state-of-the-art techniques and their clinical implications. Additionally, it discusses the key challenges and potential solutions, offering insights into the future directions of this rapidly evolving field. Keywords: Medical image analysis, Deep learning, Disease diagnosis, Lesion segmentation, Treatment planning, Data scarcity, Annotation costs, Interpretability, Robustness, State-of-the-art techniques, Clinical implications, Challenges, Solutions, Future directions.
How to Cite
Sonam Chambers, Shailendra Kumar Shriwastava. (1). MEDICAL IMAGE ANALYSIS USING DEEP LEARNING: APPLICATIONS AND CHALLENGES. ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING ISSN: 2456-1037 IF:8.20, ELJIF: 6.194(10/2018), Peer Reviewed and Refereed Journal, UGC APPROVED NO. 48767, 9(5), 14-22. Retrieved from http://ajeee.co.in/index.php/ajeee/article/view/4507
Section
Articles