Skip to content
Log in
Review

Exploring Deep Transfer Learning for Medical Image Processing and Analysis: A Comprehensive Analysis across Modalities.

M. A. S. Banu A. Dhavapandiammal K. Palanisamy
Jul 2026 · Current medical imaging · 0 citations
Medicine

TL;DR

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.

Abstract

In recent years, medical imaging has become an important tool for diagnosing diseases and disorders in healthcare. Advanced imaging technologies are being developed for non-invasive and early detection of diseases and disorders. Analyzing medical images by clinical experts is very expensive. To overcome these challenges, developing automated methods provides an effective solution. Consequently, for processing and analyzing medical images, researchers have adopted the emerging Deep Learning (DL) technologies. It has proven effective across several industries, most notably in healthcare. Even so, it has two significant limitations, such as the training cost and the large amounts of labeled data required. To reduce these limitations, Transfer Learning (TL) and Deep Learning (DL) have been integrated to create Deep Transfer Learning (DTL). This reduces the need to start from scratch and eliminates dependencies by leveraging knowledge from a source task to a target task during training, using fewer datasets. This review addresses the definitions, concepts, modalities, tasks, and techniques of DTL, along with public and private datasets used as source and target data in network-based medical imaging approaches. It also categorizes the last seven years of research by human anatomical area. It offers readers comprehensive coverage of technological advancements, future research directions, and challenges. It also reviews DTL methods by discussing those that have been applied, including Federated Learning (FL) for DL. 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.

DOI PubMed Semantic Scholar
View source

Similar papers

Open access Jul 2026

Deep Learning for Medical Imaging Analysis

This paper presents a comprehensive analysis of deep learning applications in medical imaging analysis, highlighting the critical role of artificial intelligence in modern healthcare diagnostics and 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 · 0 citations
#explainable ai Review Open access Aug 2026

Deep Learning in Medical Imaging: Architectures, Clinical Applications, and Emerging Directions

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.

Lakshmi Sai Anusha Dadi, Pravallika Devi Kommana · 0 citations
Review Jul 2026

Transformative Role of Advanced Neural Computation in Clinical Image Diagnostics: A Review of Key Concepts and Applications.

Key deep learning frameworks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs) are examined for their applications in important medical imaging tasks such as image classification, segmentation, reconstruction, and disease prediction.

Jay Kumar Pandey, S. K. Verma, J. Kumar et al. · 1 citation
Review 2026

A Systematic Literature Review of Deep Learning Models for Medical Image Analysis

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 · 0 citations
Review Open access Jul 2026

A comprehensive review of efficient deep learning for clinical medical imaging deployment

Deep learning has revolutionized medical image analysis, playing a vital role in modern clinical applications. However, the deployment of large-scale models in real-world clinical settings remains challenging due to high computational costs, latency constraints, and patient data privacy concerns associated with cloud-based processing. To address these bottlenecks, this review provides a comprehensive synthesis of efficient and lightweight deep learning architectures specifically tailored for the medical domain. We categorize the landscape of modern efficient models into three primary streams: Convolutional Neural Networks (CNNs) for local inductive bias, Lightweight Transformers for global context under constrained attention, and emerging Linear Complexity Models for scalable global context. Furthermore, we examine key model compression strategies (including pruning, quantization, knowledge distillation, and low-rank factorization) and evaluate their efficacy in maintaining diagnostic performance while reducing hardware requirements. By identifying current limitations and discussing the transition toward on-device intelligence, this review serves as a roadmap for researchers and practitioners aiming to bridge the gap between high-performance AI and resource-constrained clinical environments.

C. M. Nguyen, Truong-Son Hy · 0 citations