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@article{145632,
        author = {SREEKUTTY  K and HRUDYA K P},
        title = {Computer system for automatic WCE abnormality detection and risk prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {10},
        pages = {914-919},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145632},
        abstract = {Wireless capsule endoscopy (WCE) is a new technology to review the digestive tract diseases especially for the small intestine. But the large amount of video data produced in each examination is a major problem for its further application, motivating the design of a computer-aided detection system for different diseases in WCE images. A new computer-aided system using novel features is proposed in this paper to classify WCE images automatically and predict the risk. In the feature learning stage, we first calculate the color scale invariant feature transform from the bleeding, polyp, ulcer, and normal WCE image samples separately and then apply K-means clustering on these features to composite the comprehensive visual words. In the feature coding stage, a novel saliency and adaptive locality-constrained linear coding (SALLC) algorithm is proposed to encode the images, which considers the saliency and local information about patch feature simultaneously. After the classification, system predict the risk in that situation and give some basic treatment suggestions to the patients. },
        keywords = {Adaptive coding bases, patch saliency, saliency and adaptive locality–constrained linear coding, Wireless capsule endoscopy(WCE)},
        month = {},
        }