Search Results for Hadeel Abdul-Majeed Ghazi

Abstract

Breast cancer is one of the leading causes of mortality among women worldwide and necessitates an accurate and clinically valid mammographic assessment. Although recent deep learning frameworks have achieved promising performance in breast lesion detection, many existing frameworks focus primarily on lesion localization without providing comprehensive decision-support functionalities for clinical practice. In this study, a multi-layer clinical decision support framework is developed for mammographic breast lesion assessment by integrating YOLOv8x-based detection, ROI-based quantitative image descriptors, ROI/contour-derived geometric descriptors, physician-selected Breast Imaging Reporting and Data System (BI-RADS) assessment with associated risk visualization, and physician-editable structured report generation, all within a single workflow. The framework was developed and evaluated using 501 mammographic images. The dataset was divided into 350 training, 100 validations, and 51 test images. In the standard curve-based evaluation, the detector achieved 99.50% mAP@50 and 99.16% mAP@50-95. At the fixed confidence operating point of 0.40, the detector achieved 97.87% precision, 100.00% recall and a 98.92% F1-score. The platform extends beyond automated detection into quantitative ROI analysis, physician-editable reporting, automated PDF generation, and clinician-supervised mammographic decision support. The system does not perform autonomous diagnosis or automatic BI-RADS assignment.