Deep transfer CNNs models performance evaluation using unbalanced histopathological breast cancer dataset

Abstract

Cancer is one of the top deadly diseases. Of this disease, around about 9.8 million death cause annually. It has been recorded by the American Cancer Society that every eight women die due to breast cancer in the USA. In this paper, we have identified eight different lesion categories: Benign Tumor: Adenosis-Adenoma, Fibro-Adenoma, Phyllodes-Tumor, Tubular-Adenoma, and Malignant Tumor; Ductal-Carcinoma, Lobular-Carcinoma, Mucinous-Carcinoma, Papillary-Carcinoma. The main contribution of this paper is to examine the performance of five pre-trained CNN models on an unbalanced cancer dataset for cancer prediction. The identification of different cancer tumors has been recognized by using transfer learning models namely ResNet50, ResNet101, ResNet152, VGG16, and VGG19. BreakHis dataset has four different magnifications (40x-100x-200x-400x), and used for experiments setup in this study. The total number of images for all magnification levels is 7909. The experimental results state that the pre-trained model Residual Net with different variations has worked well 91%~96% as compared to other pre-trained models. The ResNet101 architecture model has gained a multiclass identification higher than 95%. In this paper, the proposed methodology has different evaluation parameters such as accuracy, recall, and f1-score of all pre-trained models that will help to build optimal, and automated breast lesion multiclass identification.