An integrated computer-aided diagnosis BCanD model for detection, segmentation and classification of breast cancer

Abstract A fully integrated Computer-Aided Diagnosis (CAD) system involves the integration of detection, segmentation, and classification, which makes it very useful for medical applications, particularly while dealing with the detection of breast mass and its classification into malignant and benign. The carried-out research work is intended to propose a Breast Cancer Detection (BCanD) model that is an integrated CAD system, where the system is capable enough for mass detection, its segmentation, and for the classification using mammograms. The proposed integrated system utilizes deep learning based YOLO model to detect the abnormality (mass) in the mammogram, where U-net is used for segmentation of the mass, as it has the capability to produce pixel level segmentation map, and at last stage that is the classification stage deep CNN is used for the classification. The proposed system is evaluated on open-source MIAS database. For the performance evaluation of the proposed BCanD, a three-fold cross-validation test was utilized. The mass detection accuracy of the BCanD is 98.99%, MCC is 97.96%, and F1-score is 98.87%. The model is evaluated with and without automated mass segmentation to study the impact of segmentation on the suggested CAD system. The best results was observed with the segmentation with the overall accuracy of 94.20%, F1-score (Dice) of 93.60%, MCC of 88.33%, and Jaccard of 88.08%. The proposed BCanD model surpasses the latest existing deep learning-based methodologies like fuzzy classifier, CNNI-BCC etc Hence, the proposed CAD system can be implemented and used by radiologists for all the stages from detection to diagnosis of breast mass.