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