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Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Transfer learning optimization in medical imaging enhancing diagnostic accuracy with limited data sets

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pp. 1105–1116Vol. 46Issue 4-AMay 2025DOI: 10.47974/JIOS-1895XML
Received:
11 Oct 2024
Published Online:
31 May 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1895
Pages:
1105–1116

Abstract

Medical imaging is a very important part of diagnosis because it helps doctors find diseases and plan treatments accurately. But for deep learning models to work well in medical picture analysis, they need a lot of different kinds of data, which is hard to find and costs a lot of money. Using pre-trained models on big datasets from similar areas, transfer learning looks like a good way to solve this problem. This paper looks into how to use transfer learning optimization methods to improve the accuracy of medical imaging diagnostics when samples are limited. We look into a number of different approaches, such as fine-tuning, feature extraction, and changes to the network design. Through real-world testing on a limited dataset, we show that transfer learning is a better way to improve model performance than starting from scratch. In addition, we look into how sample size and topic similarity affect how well transfer learning works. The results show that transfer learning could be a good way to improve the efficiency of medical imaging jobs for diagnosis, especially when there isn’t a lot of data available. Transfer learning lets you make strong and effective assessment tools even in healthcare settings with limited resources by using knowledge that is already stored in models that have already been taught.

Keywords

Transfer learningMedical imagingDiagnostic accuracyLimited data setsOptimization

Subject Classifications

68Q2568P15

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