A Short Review on Convolutional Neural Networks-Based Histopathological Breast Cancer Classification

Numerous modern Computer Assisted Diagnosis (CAD) systems often make use of a Convolutionanl Neural Network (CNN) architecture for early breast cancer diagnosis and for cancer classification. However, several CNN-based architectures have been developed with various features and characteristics. This study aims to compare and suggest some CNN-based architectures for breast cancer classification on histopathological images. Eight models based on different CNN architectures are covered by this study, such as CNN, VGG 19, ResNet 152, MobileNet V2, Inception V3, DenseNet and AlexNet architectures, while the eighth model is a hybridization between three architectures (VGG, DenseNet and MobileNet). All of the models are trained on the same dataset (the BreaKHis dataset) and the comparison is performed using the Accuracy, Recall, Precision and F1 Score metrics. The results of this study are extensively discussed by pinpointing the distinctive characteristics of each model and their own inherent strengths and weaknesses. Finally, some suggestions regarding the best models are outlined at the end of this paper.