Transfer learning based breast cancer detection and classification using AG2Convnet and Adap-BI magnification

Breast cancer (BC) has recently been a major issue. Although numerous BC detection methods are used in medical image processing, the correct detection and classification of benign and malignant micro-calcifications is problematic. This research provides an effective BC detection and classification method using AG2ConvNet and Adap-BI magnification. The input image is first obtained from BUSI and MIAS datasets. Image type conversion improves categorisation after capture. Next, data augmentation uses rotation and flipping. Data is pre-processed using sigmoid scaling and Gaussian filter (GF). Adap-BI magnifies pre-processed data, and the minimisation principle-adapted Canny edge detector detects edges. The AG2ConvNet classifier uses the edge-detected output to diagnose normal, benign, and malignant tumours from the digital mammography picture. Finally, the outbreak prediction models results are compared to existing methods to verify its efficacy. Results showed that the proposed method outperformed baseline methods. The suggested model outperforms EL-CNN, TL-DRS, and AM-SFF approaches with 95.41% accuracy. This shows the model's superiority and importance in improving breast cancer detection and classification, making it a potential medical image processing tool.