Classification of Breast Cancer Histopathological Images using DensNet201

Diagnosing and classifying breast cancer tumors is a rather complex activity for pathologists due to the heterogeneous nature of the tumor cells. The wide use of artificial intelligence (AI) and the rise of Deep Learning (DL) have led to promising results in terms of breast histopathology images classification. The outcomes depend largely on two main factors, namely, the number and quality of images. BreaKhis dataset shows an imbalance in the image classes distribution, thus generating the performance degradation of the classifier model due to a biased classification towards the majority class. In this paper, a Deep Convolution Generative Adversial Network (DCGAN) is applied to give the number of images consistence in the minority (benign) class with that of the majority (malignant) class. Data augmentation is a technique used later to create more data from the limited ones. The DenseNet201 pre-trained model is chosen and used with the concatenation of features from various DensNet blocks. Instead of considering all the layers of the pre-trained network, the features are extracted from the lower layers of DensNet201, via a global average pooling (GAP). These features are passed to the softmax classifier to classify breast cancer. The model is evaluated using a two-class BreaKhis, provided at four magnification levels 40x, 100x, 200x, and 400x. The proposed method yielded test accuracies of 96%, 95%, 88%, and 92% respectively for each magnification factor. As indicated in the results, the proposed method based on data augmentation by DCGAN and feature concatenation using DenseNet201 pre-trained models could produce an efficient prediction for breast cancer image classification.