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Endoscopy provides a major contribution to the diagnosis of theGastrointestinal Tract (GIT) diseases. With Colon Endoscopy having its certainlimitations, Wireless Capsule Endoscopy is gradually taking over it in theterms of ease and efficiency. WCE is performed with a miniature opticalendoscope which is swallowed by the patient and transmits colour imageswirelessly during its journey through the GIT, inside the body of the patient.These images are used to implement an effective and computationally efficientapproach which aims to detect the abnormal and normal tissues in the GITautomatically, and thus helps in reducing the manual work of the reviewers. Thealgorithm further aims to classify the diseased tissues into various GITdiseases that are commonly known to be affecting the tract. In this manuscript,the descriptor used for the detection of the interest points is Speeded UpRobust Features (SURF), which uses the colour information contained in theimages which is converted to CIELAB space colours for better identification.The features extracted at the interest points are then used to train and test aSupport Vector Machine (SVM), so that it automatically classifies the imagesinto normal or abnormal and further detects the specific abnormalities. SVM,along with a few parameters, gives a very high accuracy of 94.58% whileclassifying normal and abnormal images and an accuracy of 82.91% whileclassifying into multi-class. The present work is an improvement on thepreviously reported analyses which were only limited to the bi-classclassification using this approach.

SURF-SVM Based Identification and Classification of Gastrointestinal Diseases in Wireless Capsule Endoscopy