A Cloud-Based Breast Cancer Detection with Optimized Self-Attenuated Deep CNN

Abstract

The life of humans faces several disasters that sometimes may be deadly and cause severe effects on future endeavors. One major cause of lifestyle change is the several types of disease among which some are unrecognizable, and some are untreatable. Cancer is one of the chronic diseases that occur due to the uncontrollable cell division and in the category of cancer, breast cancer is the second disease with the lowest survival rate throughout the world. Prediction of breast cancer at its early stage enhances the survival rate. To maximize the survival rate and a healthy environment, researches on various techniques of breast cancer prediction have emerged. Though these researches have paved a better way for prediction, they also had some drawbacks that minimized the efficacy of the research outcomes. To overcome all these drawbacks, the proposed self-attention-based deep Convolutional Neural Network (Self-attention DCNN) is utilized and the classifier’s performance is enhanced with the Smack Echolocation Optimization (SELO). In addition, a better segmentation method called SELO-optimized ensemble masked Region-based CNN (SELO-mask RCNN) is introduced in the research that segments the region of interest to incorporate the fine-grained segmentation as well as the semantic understanding of the image. The performance of research is evaluated with [Formula: see text]-measure, Precision, and Recall that achieved 98.27%, 99.21%, and 98.99%, respectively.