Hybrid of DNN Feature Extraction and Ensemble Classification for Identification of Esophagitis and Barretts in Upper Gastrointestinal Tract Images
- Resource Type
- Conference
- Authors
- Khullar, Vikas; R.M, Veeramanickam M.; Muthukumarasamy, S.; Prabha, Chander; Pal Singh, Harjit; Pavankumar, Vadrevu
- Source
- 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3) Computer, Electronics & Electrical Engineering & their Applications (IC2E3), 2023 International Conference on. :1-7 Jun, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fields, Waves and Electromagnetics
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Deep learning
Image analysis
Precision medicine
Surgery
Artificial neural networks
Feature extraction
Gastrointestinal tract
- Language
The main focus of this work is to perform a computer vision classification method for upper gastrointestinal tract image analysis with a pre-trained deep learning features extraction stage. An open-source dataset was used containing images of upper gastrointestinal tract problems including Esophagitis (260 images) and Barretts (94 images). In the given input, image pre-processing with image re-sizing, and noise removal was applied, and then finally working extraction of features using pre-trained deep neural networks. The extracted data from the second last layer of pre-trained models represents the most prominent features. The extracted features are then classified with the help of machines and ensemble learning methods. The improved classification results have been identified using pre-trained models based on feature extraction techniques in comparison to traditional deep learning models.