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INTERNATIONAL JOURNAL OF RECENT TRENDS IN TECHNOLOGY AND ENGINEERING (IJRTTE)

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A Comprehensive Survey on Deep Learning Approaches in Medical Image Diagnosis

Vidya Santosh Wable
Research Scholar, Department of Computer Science & Engineering, Srinivas University, Mangaluru, India.
Dhiraj Hebri
Research Professor, Department of Computer Science & Engineering, Srinivas University, Mangaluru, India.
Mukund Wagh
Research Professor, School of Computing, MIT ADT, Pune, India.

Keywords: Deep learning; Medical image analysis; CNN; ViT; GAN; Automated diagnosis; Image segmentation; AI in healthcare; Federated learning; Explainable AI; Self-supervised learning

Abstract

Medical image analysis is essential in modern healthcare for early and proper diagnosis and appropriate treatment planning. Deep learning, particularly models like CNNs, Vision Transformers (ViTs), and GANs, has significantly advanced automated medical image diagnosis, outperforming traditional techniques in tasks such as classification, segmentation, and anomaly detection. This survey reviews key deep learning methods applied across imaging types like X-rays, MRI, CT, and ultrasound. It addresses challenges such as limited data, lack of interpretability, and clinical validation. In modern Era various new Emerging trends like self-supervised and consolidated learning are discussed, along with future directions to enhance diagnostic accuracy and clinical acceptance and adoption. This paper offers a concise reference for advancing AI-driven medical imaging.
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Published
2025-08-19
Pages
1-9
Issue
Vol. 4 No. 3 (2025): IJRTTE - 04 - 03
Section
Articles

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