Deep learning-enabled classification of gastric ulcers from wireless capsule endoscopic images

Deepak Bajhaiya; Sujatha Narayanan Unni

doi:10.1117/12.2622399

4 April 2022 Deep learning-enabled classification of gastric ulcers from wireless capsule endoscopic images

Proceedings Volume 12039, Medical Imaging 2022: Digital and Computational Pathology; 120391M (2022) https://doi.org/10.1117/12.2622399
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

The detection of gastric ulcers is commonly carried out during clinical interventions. It poses many challenges, such as extended time of diagnosis, requirement for clinical expertise, and background noise elimination, especially in early ulcer detection. We adopt a transfer learning-based approach to automatically detect ulcers from wireless-capsule endoscopic (WCE) images. We employed DenseNet121, a convolutional neural network-based pre-trained model for ulcer classification, and realized an optimal performance matrix for test data after training and validation. The DenseNet121 pre-trained model achieved 99.94% classification accuracy with 100% precision, 97.67% recall, and 98.82% F1-score for the test dataset that demonstrates the efficacy of the adopted deep learning model for fast and accurate gastric ulcer screening.

Conference Presentation

Citation Download Citation

Deepak Bajhaiya and Sujatha Narayanan Unni "Deep learning-enabled classification of gastric ulcers from wireless capsule endoscopic images", Proc. SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 120391M (4 April 2022); https://doi.org/10.1117/12.2622399

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available