Infrared Breast Image Segmentation Using Deep Neural Networks on Thermographic Images

Breast cancer is one of the most common and lethal types of cancer worldwide, with millions of new cases diagnosed each year. Early detection is pivotal in improving patient outcomes and significantly increases the chances of successful treatment. While traditional detection methods such as mammography are effective, they can be invasive, costly, painful, and less applicable for younger women with denser breast tissue. In this context, infrared thermography emerges as a promising, non-invasive technique for breast cancer detection. However, analyzing these images presents challenges due to noise and irrelevant information that can interfere with accurate diagnosis. In this work, we propose a method for segmenting infrared breast images using the DeepLabV3+ Convolutional Neural Network (CNN). Our approach leverages the power of deep learning to precisely delineate breast regions, enabling more accurate feature extraction for subsequent classification tasks. Results achieved an average accuracy of 98.69%, an Intersection over Union (IoU) of 97.18%, and a precision of 98.48%, demonstrating a clear improvement over previous approaches, particularly in terms of segmentation quality, making our method a robust tool for enhancing automated breast cancer detection.