A multi stage deep learning model for accurate segmentation and classification of breast lesions in mammography.

Mammography is a routine imaging technique used by radiologists to detect breast lesions, such as tumors and lumps. Precise lesion detection is critical for early treatment and diagnosis planning. Lesion detection and segmentation are still problematic due to inconsistencies in image quality and lesion properties. Hence, this work presents a new Multi-Stage Deep Lesion model (MSDLM) for enhancing the efficacy of breast lesion segmentation and classification. The suggested model is a Three-Unit Two-Parameter Gaussian model with U-Net, EfficientNetV2 B0, and a domain CNN classifier. U-Net is utilized for lesion segmentation, EfficientNetV2 B0 is employed for image deep feature extraction, and a CNN classifier is used for lesion classification. The MSDLM feature cascade is designed to enhance computational efficiency while retaining the most relevant features for breast cancer detection and identifying the minimum number of features most important in the detection and classification of breast lesions in mammograms. The Multi-Stage Deep Learning Model (MSDLM) was validated using two benchmark datasets, CBIS-DDSM and the Wisconsin breast cancer dataset. Segmentation and classification tasks were indicated by accuracy values of 97.6%, indicating reliability in breast lesion detection. Its sensitivity of 91.25% indicates its reliability in detecting positive cases, a basic requirement in medical diagnosis. It also indicated an Area Under the Curve (AUC) of 95.75%, indicating overall diagnostic performance irrespective of thresholds. The Intersection over Union (IoU) of 85.59% verifies its reliability to detect lesion areas in mammograms. MSDLM with a Gaussian distribution provides precise localization and classification of breast lesions. The MSDLM model allows for improved information flow and feature refinement. The algorithm outperforms baseline models in both effectiveness and computational cost. Its performance on two diverse datasets verifies its generalizability and enables radiologists to receive exact automated diagnoses.