Aug 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
TL;DR
Major deep learning architectures, including CNNs, residual networks, UNet, attention-based models, Vision Transformers, and hybrid approaches, along with their clinical applications are summarized and emerging directions such as self-supervised learning, Explainable AI, federated learning, and lightweight models are highlighted as promising approaches for more reliable and accessible medical image analysis.
Abstract
Medical image analysis has evolved from manually designed image features to deep learning methods that can learn
useful patterns directly from medical scans. These approaches have shown strong potential in tasks such as disease
classification, lesion detection, and image segmentation across modalities including MRI, CT, X-ray, Ultrasound, retinal
imaging, and dermoscopy. This review summarizes major deep learning architectures, including CNNs, residual networks, UNet, attention-based models, Vision Transformers, and hybrid approaches, along with their clinical applications. It also
discusses key barriers to practical adoption, such as limited annotated data, differences between imaging systems, model
interpretability, privacy concerns, and computational requirements. Finally, emerging directions such as self-supervised
learning, Explainable AI, federated learning, and lightweight models are highlighted as promising approaches for more reliable
and accessible medical image analysis.
Empirical evidence from recent studies demonstrates that fine-tuning and network-based DTL strategies, including federated learning, consistently enhance diagnostic accuracy, robustness, and generalization across multiple medical imaging modalities, particularly in data-limited clinical scenarios.
M. A. S. Banu, A. Dhavapandiammal, K. Palanisamy· Current medical imaging· 0 citations
This paper presents a comprehensive analysis of deep learning applications in medical imaging analysis. We examine the evolution of medical image processing from traditional computer vision approaches to sophisticated deep learning solutions, highlighting the critical role of artificial intelligence in modern healthcare diagnostics. Our research encompasses multiple dimensions of medical imaging analysis, including disease detection, segmentation, classification, and automated diagnosis across various imaging modalities. Through extensive evaluation across diverse medical imaging datasets, we demonstrate significant improvements in diagnostic accuracy and efficiency. Our findings show that deep learning techniques can achieve 95% accuracy in disease detection, a 40% reduction in analysis time, 85% improvement in early diagnosis, 30% reduction in false positives, and 99.9% reproducibility in results. Key contributions include the development of novel deep learning architectures, integration of multi-modal image analysis, implementation of real-time diagnostic systems, and establishment of comprehensive validation frameworks. Our findings underscore the necessity for modern medical imaging systems to incorporate sophisticated deep learning techniques to effectively handle the complexity of disease detection and diagnosis.
Gayatri Gupta, Aafila Shrivastava· Journal of Artificial Intell...· 0 citations
It is concluded that while deep learning has achieved remarkable performance in many medical imaging benchmarks, substantial work remains to ensure generalization, interpretability, and ethical deployment in real-world clinicalsettings.
Govinda Sahu· Journal of Machine Learning...· 0 citations
The continued integration of AI has the potential to improve diagnostic accuracy, streamline radiological workflows, and support more personalized patient care in the next generation of diagnostic imaging.
Mr. Vishal Walia, Mr. Honey Thakur, Ms. Ashwarya Sharma et al.· PAIN, JOINTS, SPINE· 0 citations
The need for continued innovation in explainable AI, computational efficiency, and regulatory compliance to bridge the gap between theoretical advancements and clinical implementation is underscored.
Jie Li, Li-Xin Wang· Current Healthcare Research· 0 citations
Medical imaging plays a crucial role in modern diagnostic practices, but traditional techniques often face limitations in accuracy, efficiency, and scalability. The emergence of deep learning (DL) has led to significant improvements that are transforming this field. This review discusses how DL algorithms are enhancing diagnostic imaging by improving accuracy, enabling automated analysis, and supporting personalized treatment plans. It focuses on key deep learning (DL) frameworks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The review examines their applications in important medical imaging tasks such as image classification, segmentation, reconstruction, and disease prediction. It also considers how DL techniques are integrated with tools like radiomics, data augmentation strategies, and predictive analytics models. DL methods have shown superior performance in detecting and classifying diseases like pneumonia, tuberculosis, and Alzheimer's. They also improve the quality and speed of imaging modalities such as MRI, CT, and ultrasound. Despite these advances, challenges remain in data availability, model interpretability, clinical validation, and ethical issues related to bias and privacy. Addressing these challenges is essential for the successful clinical use of DL in medical imaging. This review ends with suggestions for future directions and best practices for ethically and practically integrating DL technologies into routine healthcare.
Jay Kumar Pandey, S. K. Verma, J. Kumar et al.· Seminars in ultrasound, CT,...· 1 citation
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
