A comparative study of convolutional network models for the classification of abnormalities in mammograms

Breast cancer is the most common cancer and one of the main causes of death in women. Early diagnosis of breast cancer is essential to ensure a high chance of survival for the affected women. Computer-aided detection (CAD) systems based on convolutional neural networks (CNN) could assist in the classification of abnormalities such as masses and calcifications. In this paper, several convolutional network models for the automatic classification of pathology in mammograms are analyzed. As well as different preprocessing and tuning techniques, such as data augmentation, hyperparameter tuning, and fine-tuning are used to train the models. Finally, these models are validated on various publicly available benchmark datasets.

Automated summary

This paper examines the application of computer-aided detection systems based on convolutional neural networks in the pathological classification of breast cancer, and utilizes various preprocessing and tuning techniques to train and validate these models.