Transfer learning for medical image classification: a literature review

BACKGROUND: Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. We encourage data scientists and practitioners to use deep models (e.g.

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• 1 It has made a major contribution to medical image analysis as it overcomes the data scarcity problem as well as it saves time and hardware resources.
• 2 However, transfer learning has been arbitrarily configured in the majority of studies.
• 3 This review paper attempts to provide guidance for selecting a model and TL approaches for the medical image classification task.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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