Deep learning based gastric cancer identification

Gastric cancer is one of the most common cancers, which causes the second largest deaths worldwide. Manual pathological inspection of gastric slice is time-consuming and usually suffers from inter-observer variations. In this paper, we proposed a deep learning based framework, namely GastricNet, for automatic gastric cancer identification. The proposed network adopts different architectures for shallow and deep layers for better feature extraction. We evaluate the proposed framework on publicly available BOT gastric slice dataset. The experimental results show that our deep learning framework performs better than state-of-the-art networks like DenseNet, ResNet, and achieved an accuracy of 100% for slice-based classification.