Lesion Detection In Women Breast’s Dynamic Contrast-enhanced Magnetic Resonance Imaging Using Deep Learning

This article examines the effectiveness and efficiency of U-Net and its derived deep learning models for lesion detection in breast DCE-MRI. The models are applied to the benchmarked 100 Sagittal T2-Weighted fat-suppressed DCE-MRI slices of 20 patients and their performance is compared. Non-parametric statistical test Wilcoxon Signed Rank Test is used to analyze the significance of the quantitative results. Furthermore, Multi-Criteria Decision Analysis (MCDA) is used to evaluate overall performance focused on accuracy, precision, sensitivity, F-score, specificity, Geometric-Mean, DSC, and false-positive rate. RAUNet segmentation model achieved a high accuracy of 99.76%, sensitivity of 85.04%, precision of 90.21%, and Dice Similarity Coefficient (DSC) of 85.04%.