An AI-Based Method for Automated Breast Cancer Detection and Localization in Mammogram and Ultrasound Images

This research aims to improve the identification, classification, and segmentation of breast cancer using mammography and ultrasound images using a Refined Mask-RCNN framework. An OR operation merges the results, reducing misclassification and unnecessary biopsies. The system is deployed in the NVIDIA Jetson Nano developer kit to assist doctors and radiologists in the detection of cancerous cells. Compared to the original Mask-RCNN, the proposed method performs better in cancer identification and segmentation, showing improved metrics such as accuracy, precision, True Positive Rate (TPR), True Negative Rate (TNR), F-score, Balanced Classification Rate (BCR), Youden's index, Jaccard, and Dice coefficient, demonstrating the robustness and reliability of the combined approach in the clinical diagnosis of breast cancer.