Enhanced Lesion Classification Based on YOLO Architectures Using Thermal Breast Images on a Patient by Patient Basis

Abstract

Breast cancer classification using deep learning architectures plays a crucial role in assisting clinicians with early-stage diagnosis. In this study, we present a comprehensive evaluation of YOLO architectures-YOLOv2, YOLOv3, YOLOv4, and YOLOv5-for the classification of breast lesions in thermal breast images. By employing these architectures, we enhanced the identification of relevant regions of interest (ROIs) for lesion contouring. The dataset for this research was sourced from a publicly available repository, and divided on a patient-bypatient basis. This patient-based split enhances the robustness and clinical relevance of the model's performance compared to prior studies that relied on random data partitioning. Experimental results demonstrate that YOLOv5, Atrained with the Stochastic Gradient Descent with Momentum (SGDM) optimizer, achieved superior performance, with 0.83, 0.66, 0.97 and 0.79 for the key metrics of accuracy, precision, recall and F1-score, respectively. These results underscore the model's potential for reliable breast lesion classification and emphasize the importance of robust dataset partitioning to enhance clinical applicability.