Feature Extraction and Classification Techniques for Wireless Endoscopy Images: A Review

Wireless Capsule Endoscopy (WCE) is one of the convenient ways to observe human digestive system, like esophagus, stomach, duodenum, small intestine, large intestine, liver, gallbladder and pancreas which are involved in metabolism. There are no incision-related injuries, no anaesthetic complications, and no negative effects on the patient. The researchers started this investigation because early detection of abnormalities is essential. Through WCE imaging data, polyps, ulcers, and cancers can be identified early. Feature extraction and selection, classification, and image pre-processing are the next three main procedures used to handle the WCE images. One of the crucial phases that can gauge the efficacy of WCE picture classification and, ultimately, the disease, is the feature extraction and selection. This study investigates three feature selection approaches and nine feature extraction methods for classifying WCE photos. It also examines the benefits and drawbacks of each technique. Both of these are taken into account while deciding which approach to use in various situations. A table with a synopsis of each technique is supplied as supporting documentation. This study demonstrates that the suitable feature extraction technique for the civic DVC dataset is Local Binary Pattern combined with GLRLM (Gray Level Run-Length Matrix), ZM (Zero Shot Manipulation Net), PHOG (Pyramid Histogram of Oriented Gradients) and GLCM (Gray-Level Co-Occurrence Matrix), while the best feature selection and extraction techniques for general knowledge bases is CSRN (Cellular Simultaneous Recurrent Networks) and DELM (Deep Extreme Learning Machine) and the classification accuracy achieved is 96.5%.