Recognizing Polyps in Wireless Endoscopy Images Using Deep Stacked Auto Encoder with Constraint Image Model in Flexible Medical Sensor Platform

Abstract

In the recent past, Wireless endoscopy (WE) helps physicians to study the digestive tract at the cost of a wide range of images without surgery. The major challenge arises from the complication of robust image characterization in computer-aided WE pictorial diagnostics. The purpose of the research is to provide a biased definition of WE images and to enable medical professionals to automatically identify polyp images. In this paper, the learning feature approach called Deep Stacked Auto Encoder with Constraint Image (DSAECI) for recognizing polyps in WE images has been proposed. This DSAECI differs from the Traditional Method of Auto Encoder (TMAE) due to the introduction of constraint image, created by the nearest neighbor's image and describing inherent object structures. The multiple limitations of images force users to keep images in the same category far away, which share similar learned characteristics and images in various categories and utilized the Flexible medical sensor platform for data analysis. The learned characteristics thus retain large inter-variances and small intra-images. The average total accuracy (OA) of our WE images method is 98.00%. The full results showed that the proposed DSAECI can correctly identify polyps in a WE-image and provide definition characterization for web images. In clinical trials, this approach could be further used to help doctors interpret repetitive images.