Advances in Thermal Imaging: A Convolutional Neural Network Approach for Improved Breast Cancer Diagnosis

This study explores the application of thermal imaging in breast cancer diagnostics, presenting a novel methodology that integrates pre-processing techniques and Convolutional Neural Networks (CNN) for the classification of thermal breast images. A comprehensive dataset from the DMR Database is used, comprising balanced samples of normal and cancerous images. The images are augmented, standardized, and enhanced through data augmentation, resizing, and filtering, with crucial features extracted via the Histogram of Oriented Gradients (HOG). A CNN model, specifically constructed for this study, is then trained and tested on these processed images. The approach's effectiveness is benchmarked against other classifiers, displaying promising results with accuracy rates ranging from 95.7% to 98.5%. Future work suggests exploring the fusion of traditional and advanced imaging modalities, expanding datasets, and utilizing pre-trained models for improved diagnostic precision. This research signifies a step toward the development of real-time, efficient diagnostic tools for breast cancer, highlighting the potential impact on patient outcomes and the broader medical field.