Wireless Capsule Endoscopy Image Classification: An Explainable AI Approach

Wireless Capsule Endoscopy Image Classification: An Explainable AI Approach

Deep Learning has contributed significantly to the advances made in the fields of Medical Imaging and Computer Aided Diagnosis (CAD). Although a variety of Deep Learning (DL) models exist for the purposes of image classification in the medical domain, more analysis needs to be conducted on their dec...

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Bibliographic Details
Published in:IEEE access Vol. 11; pp. 105262 - 105280
Main Authors: Varam, Dara, Mitra, Rohan, Mkadmi, Meriam, Riyas, Radi Aman, Abuhani, Diaa Addeen, Dhou, Salam, Alzaatreh, Ayman
Format: Journal Article
Language:English
Published: Piscataway IEEE 2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Analytical models
Artificial intelligence
Classification algorithms
Datasets
Decision analysis
Deep learning
Endoscopes
Endoscopy
explainable AI
Explainable artificial intelligence
Feature extraction
gastrointestinal diseases
Gastrointestinal tract
Image classification
Machine learning
Medical imaging
Solid modeling
vision transformer
wireless capsule endoscopy
Wireless communication
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Summary:Deep Learning has contributed significantly to the advances made in the fields of Medical Imaging and Computer Aided Diagnosis (CAD). Although a variety of Deep Learning (DL) models exist for the purposes of image classification in the medical domain, more analysis needs to be conducted on their decision-making processes. For this reason, several novel Explainable AI (XAI) techniques have been proposed in recent years to better understand DL models. Currently, medical professionals rely on visual inspections to diagnose potential diseases in endoscopic imaging in the preliminary stages. However, we believe that the use of automated systems can enhance both the efficiency for such diagnoses. The aim of this study is to increase the reliability of model predictions within the field of endoscopic imaging by implementing several transfer learning models on a balanced subset of Kvasir-capsule, a Wireless Capsule Endoscopy imaging dataset. This subset includes the top 9 classes of the dataset for training and testing. The results obtained were an F1-score of 97% ±1% for the Vision Transformer model, although other models such as MobileNetv3Large and ResNet152v2 were also able to achieve F1-scores of over 90%. These are currently the highest-reported metrics on this data, improving upon prior studies done on the same dataset. The heatmaps of several XAI techniques, including GradCAM, GradCAM++, LayersCAM, LIME, and SHAP have been presented in image form and evaluated according to their highlighted regions of importance. This is in an effort to better understand the decisions of the top-performing DL models and look beyond their black-box nature.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3319068