Small polyp region detection in wireless capsule endoscopy (WCE) images is a challenging task in computer vision owing to two major problems: its variation in terms of shape, texture, and size, and the low illumination in the gastrointestinal tract. This study proposes a multiscale pyramidal fusion single-shot multibox detector network (MP-FSSD) to detect small polyp regions in WCE or colonoscopy frames, or both, with respect to the precision-vs-speed trade-off as the base architecture. We investigated deep transfer learning by transferring knowledge to polyp images, thereby enabling the extraction of highly representative features and contextual information from the FSSD. First, an edge-pooling layer was embedded in the shallow part of the network. Subsequently, the feature maps from different layers and scales were transformed to match their sizes. A concatenation module was introduced to integrate the feature maps from different layers, which were delivered to the next layer, followed by downsampling blocks to generate new pyramidal layers. Finally, the feature maps were fed to the multibox detectors to predict the final detection results. Experimentally, we maintained the same hyperparameters for both datasets for a fair comparison. The proposed MP-FSSD network exceeded FSSD by 3.62% in terms of mean average precision (mAP). The testing speed of 62.5 FPS is superior to that of the competitor detection methods. The proposal demonstrates that deep learning has much room for development in the field of gastrointestinal image detection.

Wireless capsule endoscopy (WCE) has revolutionised the diagnosis and treatment of gastrointestinal tract, especially the small intestine which is unreachable by traditional endoscopies. The drawback of the WCE is that it produces a large number of images to be inspected by the clinicians. Hence, the design of a computer‐aided diagnosis (CAD) system will have a great potential to help reduce the diagnosis time and improve the detection accuracy. To address this problem, the authors propose a CAD system for automatic detection of ulcer in WCE images. Firstly, they enhance the input images to be better exploited in the main steps of the proposed method. Afterward, segmentation using saliency map‐based texture and colour is applied to the WCE images in order to highlight ulcerous regions. Then, inspired by the existing feature extraction approaches, a new one has been proposed for the recognition of the segmented regions. Finally, a new recognition scheme is proposed based on hidden Markov model using the classification scores of the conventional methods (support vector machine, multilayer perceptron and random forest) as observations. Experimental results with two different datasets show that the proposed method gives promising results.