Improved Medical Imaging Transfer Learning through the Conflation of Domain Features

Raphael Wanjiku, Lawrence Nderu and Michael Kimwele
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Raphael Wanjiku: Jomo Kenyatta University of Agriculture and Technology
Lawrence Nderu: Jomo Kenyatta University of Agriculture and Technology
Michael Kimwele: Jomo Kenyatta University of Agriculture and Technology

A chapter in Artificial Intelligence Tools and Applications in Embedded and Mobile Systems, 2024, pp 113-124 from Springer

Abstract: Abstract Transfer learning has made deep learning more accessible in many fields, such as medical imaging. However, data adaptation in medical imaging transfer learning remains a challenge. With the release of many pre-trained models, there is a need to address target data adaptation in these pre-trained modes. This paper proposes the use of conflation of textural features, testing it on three medical imaging datasets and two pre-trained models, among them a MobileNetV2, to demonstrate the approach’s usefulness in mobile systems. From the experiments, the selection of images with lower textural Kullback-Leibler divergence is seen to improve the performance accuracy of the models by a margin of 13.17% in LBP and 6.47% for GLCM methods. This approach ensures that the pre-trained models can be used with much confidence and assist in generating more quality data samples for effective transfer learning in medical imaging and other applications using image data.

Keywords: Transfer learning; Features conflation; Medical imaging (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-56576-2_11

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DOI: 10.1007/978-3-031-56576-2_11

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