A Lightweight Method for Breast Cancer Detection Using Thermography Images with Optimized CNN Feature and Efficient Classification.

Autor: Nguyen Chi T; Institute of Information Technology, AMST, Hanoi, Vietnam., Le Thi Thu H; Institute of Information Technology, AMST, Hanoi, Vietnam. hongltt@ioit.ai.vn., Doan Quang T; Department of Computing Fundamental, FPT University, Hoa Lac High Tech Park, Hanoi, Vietnam., Taniar D; Faculty of Information Technology, Monash University, Melbourne, Australia.
Jazyk: angličtina
Zdroj: Journal of imaging informatics in medicine [J Imaging Inform Med] 2024 Oct 02. Date of Electronic Publication: 2024 Oct 02.
DOI: 10.1007/s10278-024-01269-6
Abstrakt: Breast cancer is a prominent cause of death among women worldwide. Infrared thermography, due to its cost-effectiveness and non-ionizing radiation, has emerged as a promising tool for early breast cancer diagnosis. This article presents a hybrid model approach for breast cancer detection using thermography images, designed to process and classify these images into healthy or cancerous categories, thus supporting disease diagnosis. Multiple pre-trained convolutional neural networks are employed for image feature extraction, and feature filter methods are proposed for feature selection, with diverse classifiers utilized for image classification. Evaluating the DRM-IR test set revealed that the combination of ResNet34, Chi-square ( χ 2 ) filter, and SVM classifier demonstrated superior performance, achieving the highest accuracy at 99.62 % . Furthermore, the highest accuracy improvement obtained was 18.3 % when using the SVM classifier and Chi-square filter compared to regular convolutional neural networks. The results confirmed that the proposed method, with its high accuracy and lightweight model, outperforms state-of-the-art breast cancer detection from thermography image methods, making it a good choice for computer-aided diagnosis.
(© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)
Databáze: MEDLINE
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