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Wireless capsule endoscopy (WCE) is a technology that uses a pill-sized camera to visualize images of the digestive tract. It presents several advantages, since it is far less invasive, does not require sedation and has less potential complications compared to standard endoscopy. Hence, it might be exploited as alternative to the standard procedure. WCE is used to diagnosis a variety of gastro-intestinal diseases such as polyps, ulcers, Crohn's disease and hemorrhages. Nevertheless, WCE videos can contain thousands of images per patient that must be screened by medical specialists, besides, the capsule free mobility and technological limits cause production of a low quality images. In this paper, a Nouvel method based on Dense-UNet deep learning segmentation model is presented. This approach aims at red lesion, ulcer and polyp detection from WCE images. Then, we propose a modified residual attention network for images classification. The proposed methods training and validation accuracies are 97.57% and 92.70%, with training and validation intersection over union and dice coefficients of 75.31%, 71.29%, 83.50% and 80.66%, respectively, in the red lesion dataset. In the polyp dataset, the method achieved a training and validation accuracies of 98.26% and 92.33%, with training and validation intersection over union and dice coefficients of 92.09%, 95.62%, 80.13% and 87.14%, respectively. Besides, the proposed architecture reached an average accuracy of 99.30%, sensitivity, specificity and F1 score of 98.61%, 100% and 99.30%, respectively. These results demonstrate that the proposed approach is satisfactory in polyp and red lesion segmentation and ulcer detection. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) is a novel imaging technique that can view the entire small bowel in human body. Therefore, it has been gradually adopted compared with traditional endoscopies for gastrointestinal diseases. However, the task of reviewing the vast amount of images produced by a WCE test is exhaustive for the physicians. This paper presents a new feature extraction scheme for pathological inflammation and ulcer regions discrimination in WCE images. In addition, the novel approach is adopted for polyp recognition in colonoscopy videos. A novel idea based on extracting certain local features from the image is proposed. Then, the occurrence histogram of these features is used as descriptor of the image. The new feature descriptor scheme is grayscale rotation invariant and computationally simple as the operator can be realized with a few operations in a small neighborhood. The proposed operator does not discard the contrast information. Besides, we propose to test the quality of the model using logarithmic loss metric and show how calibration can be useful in reducing the aforementioned measure. Extensive classification experiments have been applied on different datasets, which prove that the occurrence histogram of the extracted features is powerful. The proposed method achieved 99.1%, 99.7% and 99.2% in terms of the precision in the first, second and third datasets, respectively, and surpassed some known local descriptors on a texture dataset. [ABSTRACT FROM AUTHOR]