Detection of Early Gastric Cancer Based on Single Shot Detector with Feature Enhancement

Endoscopy is one of the most commonly used tools for gastrointestinal examination. However, due to its close dependence on the experience of operators, the missed diagnosis rate of early gastric cancer (EGC) is still high. Therefore, the auxiliary detection of EGC based on endoscopic images is of great significance. In order to better fuse low-level features with rich details and high-level features with sufficient semantics, in this paper, we propose a deeper-shallower feature enhancement (DSF) module, in which the feature maps from both deeper layer and shallower layer are combined to enhance the features of current layer. Then the enhanced features are used as the next shallower input in the bottom-up path feature fusion (BUF) module, which transmits the enhanced features in a bottom-up manner. In order to capture features that focus more on accurate information, the channel-wise attention (CA) module is applied before prediction. Moreover, an endoscopic image dataset that contains four categories of lesions is established for implementing the task of EGC detection. Experimentally, the proposed method achieves the fl-scores of 90.65%, 63.34% and 89.95% on Kvasir dataset, ETIS-Larib dataset and our endoscopic dataset respectively, outperforming the baseline by 0.62%, 3.14% and 2.03% respectively.