Breast Mass Detection and Classification Using Transfer Learning on OPTIMAM Dataset Through RadImageNet Weights

Abstract

A significant number of women are diagnosed with breast cancer each year. Early detection of breast masses is crucial in improving patient prognosis and survival rates. In recent years, deep learning techniques, particularly object detection models, have shown remarkable success in medical imaging, providing promising tools for the early detection of breast masses. This paper uses transfer learning methodologies to present an end-to-end breast mass detection and classification pipeline. Our approach involves a two-step process: initial detection of breast masses using variants of the YOLO object detection models, followed by classification of the detected masses into benign or malignant categories. We used a subset of OPTIMAM (OMI-DB) dataset for our study. We leveraged the weights of RadImageNet, a set of models specifically trained on medical images, to enhance our object detection models. Among the publicly available RadImageNet weights, DenseNet-121 coupled with the yolov5m model gives 0.718 mean average precision(mAP) at 0.5 IoU threshold and a True Positive Rate (TPR) of 0.97 at 0.85 False Positives Per Image (FPPI). For the classification task, we implement a transfer learning approach with fine-tuning, demonstrating the ability to effectively classify breast masses into benign and malignant categories. We used a combination of class weighting and weight decay methods to tackle the class imbalance problem for the classification task.