Improved Mask R-CNN Segmentation and Bayesian Interactive Adaboost CNN Classification for Breast Cancer Detection on Bach Dataset

Abstract

Breast cancer is considered as the predominant type of cancer that affects more than ten percentage of the worldwide female population. Though microscopic evaluation remains to be a significant method for diagnosing, time and cost complexity seeks alternative and effective computer aided design for rapid and more accurate detection of the disease. As DL (Deep Learning) possess a significant contribution in accomplishing machine automation, this study intends to resolve existing problems with regard to lack of accuracy by proposing DL based algorithms. The study proposes Improved-Mask R CNN (I-MRCNN) method for segmentation. In this process, RPN (Region Proposal Network), predicts the objectless scores and object bound at every position. Here, (RoI Align) Region of interest Align is used for feature extraction as it is capable of resolving the conventional RoI pooling issues by attaining high accuracy for small objects and also eliminates quantization issues. Further, classification is performed using the proposed Bayesian Interactive Adaboost CNN classifier (B-IAB- CNN) that integrates the advantages of CNN, Bayesian and Adaboost classifier. The advantages of the three classifier enable optimum classification of the input Bach dataset that is confirmed through the results of performance analysis of the proposed system. Outcomes reveal that, average accuracy for segmentation is 96.32%, while, the classification accuracy is exposed to be 96%. As Timely prediction is significant, high prediction rate of the proposed system will assist the medical practitioners to detect breast cancer quickly which is the important practical implication from this study for diagnosing breast cancer.