Open Access

ARTICLE

Anatomical Region Detection Scheme Using Deep Learning Model in Video Capsule Endoscope

S. Rajagopal^1,*, T. Ramakrishnan², S. Vairaprakash³

1 Department of Information Technology, National Engineering College, Kovilpatti, 628503, Tamilnadu, India
2 Department of Electronics and Instrumentation Engg., National Engineering College, Kovilpatti, 628503, Tamilnadu, India
3 Department of Electronics and Communication Engg., Ramco Institute of Technology, Rajapalayam, 626117, Tamilnadu, India

* Corresponding Author: S. Rajagopal. Email:

Intelligent Automation & Soft Computing 2022, 34(3), 1927-1941. https://doi.org/10.32604/iasc.2022.024998

Received 07 November 2021; Accepted 19 January 2022; Issue published 25 May 2022

Abstract

Video capsule endoscope (VCE) is a developing methodology, which permits analysis of the full gastrointestinal (GI) tract with minimum intrusion. Although VCE permits for profound analysis, evaluating and analyzing for long hours of images is tiresome and cost-inefficient. To achieve automatic VCE-dependent GI disease detection, identifying the anatomical region shall permit for a more concentrated examination and abnormality identification in each area of the GI tract. Hence we proposed a hybrid (Long-short term memory-Visual Geometry Group network) LSTM-VGGNET based classification for the identification of the anatomical area inside the gastrointestinal tract caught by VCE images. The video input data is converted to frames such that the converted frame images are taken and are processed. The processing and classification of health condition data are done by the use of Artificial intelligence (AI) techniques. In this paper, we proposed a prediction of medical abnormality from medical video data that includes the following stages as given: Pre-processing stage performs using Gabor filtering, histogram-based enhancement technique is employed for the enhancement of the image. Multi-linear component analysis-based feature selection is employed, and the classification stage performs using Hybrid LSTM-VGGNET with the performance of accurate prediction rate.

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Keywords

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