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

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 ( \(\varvec{\chi }^{\varvec{2}}\) ${\mathit{\chi }}^{\mathbf{2}}$ ) filter, and SVM classifier demonstrated superior performance, achieving the highest accuracy at \(\varvec{99.62\%}\) $\mathbf{99.62}\mathbf{\%}$ . Furthermore, the highest accuracy improvement obtained was \(\varvec{18.3\%}\) $\mathbf{18.3}\mathbf{\%}$ 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.