Rapid tri-net: breast cancer classification from histology images using rapid tri-attention network

Nowadays, people all over the world are facing several problems related to the deadly disease of breast cancer. The research on breast cancer detection using existing techniques shows lesser detection results due to the shortage of medical facilities and manual detection procedures. However, the disease diagnosis becomes very critical when it is detected in the critical or chronic stage. Early detection plays an essential role in the accurate detection and effective treatment of breast cancer, which further minimizes the death rate. Therefore, automated breast cancer detection based on deep learning is proposed in this paper by using histopathological images. The proposed approach involves five steps: image filtering, dual-stage segmentation, feature extraction, feature selection, and classification. Initially, image filtering is performed to execute image resizing, noise elimination, and contrast enhancement. The weighted Guided Image Filtering (Weighted GIF) approach is used for noise removal, and Transformed Optimal Gamma Correction (TOGC) is used for contrast enhancement. To obtain the cellular structures from histology/histopathology images, a dual-stage segmentation using Superpixel Mixed Clustering (SMC) is applied. Then, feature extraction is done by the Gray Level Co-occurrence Matrix with Three-dimensional space (GLCM -3D) and RGB color model to extract texture and color features. Then, the most significant features are selected using Stochastic Diffusion Dynamic Optimization (SDDO). Finally, breast histology images have been classified using Rapid Tri-Attention Residual Dense Capsule Network with Aquila Optimization (Rapid Tri-Net), further categorizing the histology images into various classes. The proposed approach is simulated in the Python platform using BreakHis and BACH datasets and evaluates the performance on the basis of f-measure, recall, accuracy, precision, and specificity. Rapid Tri-Net's performance is related to the recent prevailing framework to attain a fair comparison. As a result, the simulated results clearly showed that the proposed Rapid Tri-Net performed better than the existing approaches.