Evaluation of deep learning models for detecting breast cancer using histopathological mammograms Images

Abstract

Breast cancer detection based on the deep learning approach has gained much interest among other conventional-based CAD systems as the conventional based CAD system's accuracy results seems to be inadequate. The convolution neural network, a deep learning approach, has emerged as the most promising technique for detecting cancer in mammograms. In this paper we delve into some of the CNN classifiers used to detect breast cancer by classifying mammogram images into benign, cancer, or normal class. Our study evaluated the performance of various CNN architectures such as AlexNet, VGG16, and ResNet50 by training some of them from scratch and some using transfer learning with pre-trained weights. The above model classifiers are trained and tested using mammogram images from the mini-DDSM dataset which is publicly available. The medical dataset contains limited samples of data due to low patient volume; this can lead to overfitting issue, so to overcome this limitation data augmentation process is applied. Rotation and zooming techniques are applied to increase the data volume. The validation strategy used here is 90:10 ratio. AlexNet showed an accuracy of 65 percent, whereas VGG16 and ResNet50 showed an accuracy of 65% and 61%, respectively when fine-tuned with pre-trained weights. VGG16 performed significantly worse when trained from scratch, whereas AlexNet outperformed others. VGG16 and ResNet50 performed well when transfer learning was applied.