Performance improvement for medical image classification model by using gradient-based analytical feature selection

Ryo Toda; Hayato Itoh; Masahiro Oda; Yuichiro Hayashi; Yoshito Otake; Masahiro Hashimoto; Toshiaki Akashi; Shigeki Aoki; Kensaku Mori

doi:10.1117/12.3006620

3 April 2024 Performance improvement for medical image classification model by using gradient-based analytical feature selection

Ryo Toda, Hayato Itoh, Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, Kensaku Mori

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129272C (2024) https://doi.org/10.1117/12.3006620
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

This paper presents a gradient-based analytical method for improving medical image classification. The automated classification of diseases is important in computer-aided diagnosis. In addition to accurate classification, its explainability is an essential facto. A gradient-based visual explanation provides the explainability of a model of an convolutional neural network (CNN). Most studies use this explanation to assess CNN's validity in a qualitative manner. Our motivation is to utilize the visual-explanation methods to enhance the classification accuracy of a CNN model. We propose a weight-analysis-based method to improve the classification accuracy of a trained-CNN model. The proposed method selects important patterns based on a gradient-based weight analysis of a middle layer in a trained model and suppresses irrelevant patterns in the extracted features for the classification. We applied our analytical method to a convolutional and a global-average-pooling layers in a CNN, which classifies a chest CT volume into COVID-19 typical and non-typical cases. As shown in classification results on 302 testing cases, our method improved the accuracy of the COVID-19 classification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Ryo Toda, Hayato Itoh, Masahiro Oda, Yuichiro Hayashi, Yoshito Otake, Masahiro Hashimoto, Toshiaki Akashi, Shigeki Aoki, and Kensaku Mori "Performance improvement for medical image classification model by using gradient-based analytical feature selection", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129272C (3 April 2024); https://doi.org/10.1117/12.3006620

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