The Comparative Study of CNN models for Breast Histopathological Image Classification
R. K. Chandana Mani, J Kamalakannan · 2023
In Recent years, breast cancer has been considered as the prevalent type of cancer and second deadliest cancer. Histopathological image analysis is the gold standard for cancer diagnosis. Since CAD systems take less time compared to traditional approaches it is notable that automated cancer classification will be helpful for reliable and accurate diagnosis. Also CAD models assisted with deep learning techniques produce excellent results for classification of breast histopathological images. Convolutional Neural Network (CNN) models have achieved significant progress in medical image classification tasks including breast histology images. This paper makes a brief comparative study that illustrates performance of CNNs such as VGG-16, ResNet-50 and AlexNet for breast histological image classification. The study used BreakHis dataset for its model comparison. Further the paper illustrates application of data augmentation methods for the dataset as it improves the model accuracy. Also we extend our study to transfer learning methods and cross-check the results after using pre-trained models for breast histology image classification.