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RAt-CapsNet: A Deep Learning Network Utilizing Attention and Regional Information for Abnormality Detection in
Md Jahin Alam1, Rifat Bin Rashid1, Shaikh Anowarul Fattah1
1Department of Electrical and Electronic EngineeringBangladesh University of Engineering and Technology Dhaka 1000 Bangladesh.
IEEE Journal of Translational Engineering in Health and Medicine
|August 29, 2022
Summary
A new RAt-CapsNet model enhances wireless capsule endoscopy (WCE) analysis by using regional context and attention mechanisms. This AI system accurately detects gastrointestinal abnormalities, improving diagnostic efficiency.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Gastroenterology
Background:
- Wireless capsule endoscopy (WCE) offers non-invasive gastrointestinal disease identification.
- Manual inspection of WCE videos is time-consuming, necessitating automated systems.
- Low resolution and limited regional context in WCE images pose significant challenges.
Purpose of the Study:
- To develop an automated system for detecting abnormalities in WCE videos.
- To address challenges of low resolution and lack of regional context in WCE images.
- To improve the efficiency and accuracy of WCE data analysis.
Main Methods:
- A novel Convolutional Neural Network (CNN) architecture, RAt-CapsNet, was proposed.
- RAt-CapsNet utilizes a Volumetric Attention Mechanism for 3D feature enhancement.
- A Pyramid Feature Extractor processes image-driven feature vectors to capture local pixel relationships.
Main Results:
- The RAt-CapsNet achieved a mean accuracy of 98.51% for binary classification.
- Multi-class classification accuracy exceeded 95.65%.
- Experiments were conducted on a large, unbalanced dataset of over 47,000 labeled images.
Conclusions:
- The proposed RAt-CapsNet demonstrates high efficacy in WCE abnormality detection.
- The methodology offers a noteworthy advancement in WCE diagnostic systems.
- The integration of regional information and attention mechanisms improves diagnostic accuracy.
