Advanced Deep Learning Model for Breast Cancer Detection via Thermographic Imaging

Breast cancer remains one of the primary causes of morbidity and mortality among women globally. As a result, there is a need to create precise, non-intrusive, and affordable screening techniques. This study aims to introduce a novel method for breast cancer detection using thermographic images, leveraging advanced deep learning techniques. The suggested method uses the VGG16 model to pull out features, bidirectional long short-term memory (BLSTM) to see how features change over time, and an attention mechanism to make sure that important features stand out more. The proposed method demonstrates superior performance over existing techniques. The model's high sensitivity and F1-Score highlight its potential for early breast cancer detection, which can lead to a reduction in false negatives and ultimately improve patient outcomes.