Transfer Learning in Deep Learning Models for Medical Imaging: Utilizing Pretrained Models to Improve Performance in Medical Image Analysis


Year : 2025 | Volume : 03 | Issue : 01 | Page : 67-85
    By

    Vedant Singh,

  1. Student, Department of Computer Science, University of California, San Diego, United States

Abstract

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Transfer learning is now a trending technique in deep learning, especially in medical imaging. This technique solves landmark problems by utilizing the pre-trained models, including the limited availability of the annotated medical data and the time-consuming computational costs of training deep learning models from scratch. The generalizability of deep models could increase diagnostic precision for specific medical tasks, require fewer samples to train, and take less time to train due to transfer learning. This study shares different kinds of transfer learning techniques, namely feature extraction, fine-tuning, and domain adaptation, with much focus on disease diagnoses such as pneumonia, tumours, and diabetic retinopathy. Using ResNet and other models, the improvement of clinical outcomes has been demonstrated in a few cases, including the use of VGG16 and Inception. Moreover, the combination of the newcomer’s generative adversarial networks (GANs) and hybrid models with the more conventional machine learning boosts the models’ stability. However, there is no denying that problems such as data bias, overfitting, and AI model interpretability are significant. On this subject, the paper addresses principles such as patient confidentiality as important when it comes to AI in healthcare. Future directions focus on evolving different deep learning structures and including multiple types of data to enhance diagnostic performance. Transfer learning brings revolutionary changes to medical imaging with the improvement in efficiency, reliability, and patient experience.

Keywords: Transfer learning, deep learning, medical imaging, pre-trained models, convolutional neural networks (CNNS), diagnostic accuracy, feature extraction, generative adversarial networks (GANS), ethical considerations, multi-modal data integration

[This article belongs to International Journal of Image Processing and Pattern Recognition (ijippr)]

How to cite this article:
Vedant Singh. Transfer Learning in Deep Learning Models for Medical Imaging: Utilizing Pretrained Models to Improve Performance in Medical Image Analysis. International Journal of Image Processing and Pattern Recognition. 2025; 03(01):67-85.
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Vedant Singh. Transfer Learning in Deep Learning Models for Medical Imaging: Utilizing Pretrained Models to Improve Performance in Medical Image Analysis. International Journal of Image Processing and Pattern Recognition. 2025; 03(01):67-85. Available from: https://journals.stmjournals.com/ijippr/article=2025/view=0


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Regular Issue Subscription Original Research
Volume 03
Issue 01
Received 31/12/2024
Accepted 05/01/2025
Published 07/02/2025
Publication Time 38 Days
93

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