Optimal Trained Deep Learning Model for Breast Cancer Segmentation and Classification

Abstract

Breast cancer is the most widespread cancer among women. Based on the International cancer research center analysis, the highest number of deaths among women is due to breast cancer. Hence, detecting breast cancer at the earliest may help the oncologist to make appropriate decisions. Due to variations in breast tissue density, there is still a challenge in precise diagnosis and classification. To overcome this challenge, a novel OTDEM-based breast cancer segmentation and classification is proposed with the following four stages: they are, preprocessing, segmentation, feature extraction and classification. The input image is passed to the initial stage using the CLAHE filter to enhance the image. Then the preprocessed image is given to the segmentation stage for the image sub-segments by correlation-based deep joint segmentation. Following that, the features such as statistical features, improved LGXP, texton features, and shape-based features are derived from the segmented image. Then the derived features are fed to the ensemble model that includes CNN, DBN, and BI-GRU classifier to finalize the classification outcome. Further, to enhance the performance of the ensemble model, the weight of BI-GRU is optimized via a new algorithm termed SIPOA. This ensures optimal training to make the model more appropriate in its classification process. Finally, the performance of the proposed work is validated over the traditional models concerning different performance measures.