Exploring Deep Transfer Learning for Medical Image Processing and Analysis: A Comprehensive Analysis across Modalities
- 1Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Dindigul, India
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.
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BibTeX
@article{banu2026exploring,
title = {{Exploring Deep Transfer Learning for Medical Image Processing and Analysis: A Comprehensive Analysis across Modalities}},
author = {M.A. Sithi Banu and A. Dhavapandiammal and Kalavathi Palanisamy},
journal = {Current Medical Imaging},
year = {2026},
month = jul,
doi = {10.2174/0115734056456208260706064206},
url = {https://doi.org/10.2174/0115734056456208260706064206},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Exploring Deep Transfer Learning for Medical Image Processing and Analysis: A Comprehensive Analysis across Modalities
AU - M.A. Sithi Banu
AU - A. Dhavapandiammal
AU - Kalavathi Palanisamy
PY - 2026
DA - 2026/07/14
JO - Current Medical Imaging
DO - 10.2174/0115734056456208260706064206
UR - https://doi.org/10.2174/0115734056456208260706064206
AB - 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.
SN - 1573-4056
ER -