Analysis of the effects of image quality differences on CAD performance in AI-based benign-malignant discrimination processing of breast masses

Kazuya Abe; Soma Kudo; Hideya Takeo; Yuichi Nagai; Shigeru Nawano

doi:10.1117/12.2623398

13 July 2022 Analysis of the effects of image quality differences on CAD performance in AI-based benign-malignant discrimination processing of breast masses

Kazuya Abe, Soma Kudo, Hideya Takeo, Yuichi Nagai, Shigeru Nawano

Author Affiliations +

Proceedings Volume 12286, 16th International Workshop on Breast Imaging (IWBI2022); 122860R (2022) https://doi.org/10.1117/12.2623398
Event: Sixteenth International Workshop on Breast Imaging, 2022, Leuven, Belgium

Abstract

In recent years, the amount of images to be read has increased due to the higher resolution of diagnostic imaging devices, and the burden on doctors has also increased. To solve this problem, the improvement of CAD (computer-aided diagnosis) performance has been studied. In this study, we developed an AI-based system for discriminating benign and malignant breast cancer tumors using transfer learning, one of the deep learning methods of AI, and analyzed what factors are necessary to improve the diagnostic accuracy of the system. Classification of benign and malignant diseases using diagnostic images showed an accuracy of 90%, which was equivalent to physician's discrimination, but the accuracy for medical checkup images was low at 85%, and image comparison revealed that this was due to noise and low contrast. We analyzed that these improvements are necessary for the construction of a more accurate CAD system for medical checkup images.

Citation Download Citation

Kazuya Abe, Soma Kudo, Hideya Takeo, Yuichi Nagai, and Shigeru Nawano "Analysis of the effects of image quality differences on CAD performance in AI-based benign-malignant discrimination processing of breast masses", Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 122860R (13 July 2022); https://doi.org/10.1117/12.2623398

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