An Early Detection and Classification of Breast Cancer Using Weight based AdaBoost Algorithm

Abstract

Breast cancer is a deadly disease; an accurate and early diagnosis of breast cancer is the most efficient method to decrease the death rate. But, in the early detection and diagnosis of breast cancer, differentiating abnormal tissues is a challenging task. In this paper, a weight-based AdaBoost algorithm is proposed for an effective detection and classification of the breast cancer. An AdaBoost algorithm effectively classifies the breast cancer classes by adding the weights to the samples in the week classifier during the training phase. A weighted vote is performed on the results of each week classifier, and the strong classifier is integrated according to the weight of the week classifier. The breast cancer image datasets named CBIS-DDSM and MIAS are utilized for effective classification. Tumor-like regions (TLRs) are diagnosed by utilizing the optimum method of Otsu thresholding to enhance training abilities. The convolutional neural network (CNN) architectures of the AlexNet and ResNet50 are utilized for the feature extraction. A weight-based AdaBoost algorithm is proposed for the classification of breast cancer mammogram images into four classes benign calcification (BC), malignant calcification (MC), benign mass (BM) as well and malignant mass (MM).The results shows that the proposed weight based AdaBoost algorithm delivers the performance metrics such as accuracy, specificity, sensitivity, precision and F1-score values about 99.56%, 99.38%, 99.40%, 98.89% and 99.18% respectively, which ensures the accurate classification results compared with the existing methods such as IMPA-ResNet50, Gray difference weight and MSER detector, MLO and CC methods.