Abstract: With the full application of Wireless Capsule Endoscopy (WCE) in the detection of gastrointestinal diseases, screening out a small number of lesion images from the massive imaging data brings a heavy burden to doctors. To solve the problems existing in the automatic detection of WCE images, such as inconspicuous colour and texture features, ease of being confused with healthy organs, fuzzy detail features and different sizes of lesions, and high impurities, we propose a residual-based multi-scale fully convolutional neural network to classify and detect lesions in WCE image. By introducing the concepts of skip connection in residual learning network and multi-scale convolution kernel in the inception network, the model can effectively extract the detailed features of various lesions in the image. The experimental results show that the sensitivity of the model reaches 98.05%, the specificity reaches 97.67%, and the accuracy reaches 97.84%. It is better than the classical deep residual network ResNet50 and the standard width multi-scale Inception-v4 algorithm. The model has high recognition rate, fast convergence speed, and improved computing performance. In short, the algorithm model takes into account the efficiency and performance of bleeding  point detection, and has strong practicability.

Key words: deep learning, wireless capsule endoscopy, convolutional neural network, residual network, multiple convolution kernels