RNN model-based classification of wireless capsule endoscopy bleeding images

C. E. Mohankumar; S. V. Dharani Kumar; S Senthilkumar; V Sornagopal; M S Maharajan

doi:10.53730/ijhs.v6nS1.6910

Authors

C.E. Mohankumar Assistant Professor, Department of ECE, GRT Institute of Engineering and Technology, Tiruttani
S.V. Dharani Kumar Assistant Professor, Department of ECE, GRT Institute of Engineering and Technology, Tiruttani
Senthilkumar. S Associate Professor, Department of ECE, GRT Institute of Engineering and Technology, Tiruttani
Sornagopal V Associate Professor, Department of ECE, GRT Institute of Engineering and Technology, Tiruttani
Maharajan M S Assistant Professor, Department of CSE, GRT Institute of Engineering and Technology, Tiruttani

Keywords:

Endoscopy, RNN, Machine Learning

Abstract

WCE (wireless capsule endoscopy) is a technique that may be used to diagnose gastrointestinal issues and provide painless gut imaging. Regardless, a variety of variables, such as effectiveness, tolerance, safety, and performance, make widespread use and modification challenging. Furthermore, automated analysis of the WCE data is essential for detecting anomalies. When a patient's digestive system is imaged using WCE, a vast amount of data is generated and these challenges have been addressed using a variety of computer assisted and vision-based technologies, but they do not achieve the essential level of precision, and further work is required. With this work, the goal is to create a system that can automatically analyze WCE images to identify problems and assist practitioners in making right diagnoses. Finally, a comparison of SODM-S1 with SODM shows that by modifying features to increase spatial dependency, our suggested technique may really improve model performance.

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