Gastrointestinal tract (GIT) infections such as ulcers, bleeding, polyps, Crohn’s disease and cancer are quite familiar today worldwide. Wireless capsule endoscopy (WCE) is an efficient means of in...

Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images

Wireless capsule endoscopy (WCE) is an efficient tool to investigate gastrointestinal tract disorders and perform painless imaging of the intestine. Despite that, several concerns make its wide applicability and adaptation challenging like efficacy, tolerance, safety, and performance. Besides, automatic analysis of the WCE provided dataset is of great importance for detecting abnormalities. Imaging of the patient’s digestive tract through WCE produces a large dataset that requires a substantial amount of time and a special skill set from a medical practitioner for analysis. Several computer-aided and vision-based solutions have been proposed to resolve these issues, yet, they do not provide the desired level of accuracy and further improvements are still needed. The current study aims to devise a system that can perform the task of automatic analysis of WCE images to identify abnormalities and assist practitioners for robust diagnosis. This study adopts a deep neural network approach and proposes a model name BIR (bleedy image recognizer) that combines the MobileNet with a custom-built convolutional neural network (CNN) model to classify WCE bleedy images. BIR uses the MobileNet model for initial-level computation for its lower computation power requirement and subsequently the output is fed to the CNN for further processing. A dataset of 1650 WCE images is used to train and test the model using the measures of accuracy, precision, recall, F1 score, and Cohen’s kappa to evaluate the performance of the BIR. Results indicate the promising outcomes with achieved accuracy, precision, recall, F1 score, and Cohen’s kappa of 0.993, 1.000, 0.994, 0.997, and 0.995 respectively. The accuracy of the BIR model is 0.978 with the Google collected WCE image dataset which is better than the state-of-art approaches.

Wireless Capsule Endoscopy Bleeding Images Classification Using CNN Based Model

A survey on contemporary computer-aided tumor, polyp, and ulcer detection methods in wireless capsule endoscopy imaging

In this paper, we propose a platform to achieve accurate localization of small-bowel lesions and endoscopic video stabilization in wireless capsule endoscopy. Current research modules rely on the use of external magnetic fields and triangulation methods to calculate the position vector of the capsule, leading to considerable error margins. Our platform, entitled OdoCapsule (a synthesis of the words Odometer and Capsule), provides real-time distance information from the point of duodenal entry to the point of exit from the small bowel. To achieve this, OdoCapsule is equipped with three miniature legs. Each leg carries a soft rubber wheel, which is made with human-compliant material. These legs are extendable and retractable thanks to a micromotor and three custom-made torsion springs. The wheels are specifically designed to function as microodometers: each rotation they perform is registered. Hence, the covered distance is measured accurately in real time. Furthermore, with its legs fully extended, OdoCapsule can stabilize itself inside the small-bowel lumen thus offering smoother video capture and better image processing. Recent ex vivo testing of this concept, using porcine small bowel and a commercially available (custom-modified) capsule endoscope, has proved its viability.

OdoCapsule: next-generation wireless capsule endoscopy with accurate lesion localization and video stabilization capabilities.

Deep transfer learning approaches for bleeding detection in endoscopy images.

In recent years, Wireless capsule endoscopy (WCE) has been widely utilized in diagnosis of gastrointestinal (GI) tract disease. This new technology is painless and can see small intestine that traditional endoscopies cannot reach. However, Analysis of massive images for each WCE detection is tedious and time consuming to physicians. In this paper we present a computer-aid approach to help clinicians to discriminate amongst regions of normal or abnormal tissue. We use covariance of second-order statistical features which called as color wavelet covariance (CWC), based on discrete wavelet transform (DWT) and then optimize them by a selected algorithm. Accurate image segmentation and classification is achieved by a joint classifier, which is obtained by Texton Boost classifier. The whole approach has been validated on various WCE data and achieves a good result.

A new approach to detecting ulcer and bleeding in Wireless capsule endoscopy images

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A new approach to detecting ulcer and bleeding in Wireless capsule endoscopy images