A Deep-Learning-Based Breast Cancer Detection Method
This work contrasts several machine learning and deep learning models on the breast cancer metastases detection problem and experiments the impacts of feature extraction, dimension reduction and the sizes of the datasets on these models. This work also establishes a breast cancer metastases detection pipeline based on the most accurate model in the experiments. Among the models, Dense Model using features trained from DenseNet201 with weights trained by ImageNet is of the best performance. This work develops a computer aided method to examine the cancerous regions in the whole slide images of breast cancer more quickly and precisely than an experienced doctor does.