A Comparative Analysis of EfficientNet Architectures for Identifying Anomalies in Endoscopic Images

Alexandre Pessoa, Darlan Quintanilha, João Dallyson Sousa de Almeida, Geraldo Braz Junior, Anselmo C. de Paiva, António Cunha

2024

Abstract

The gastrointestinal tract is part of the digestive system, fundamental to digestion. Digestive problems can be symptoms of chronic illnesses like cancer and should be treated seriously. Endoscopic exams in the tract make detecting these diseases in their initial stages possible, enabling an effective treatment. Modern endoscopy has evolved into the Wireless Capsule Endoscopy procedure, where patients ingest a capsule with a camera. This type of exam usually exports videos up to 8 hours in length. Support systems for specialists to detect and diagnose pathologies in this type of exam are desired. This work uses a rarely used dataset, the ERS dataset, containing 121.399 labelled images, to evaluate three models from the EfficientNet family of architectures for the binary classification of Endoscopic images. The models were evaluated in a 5-fold cross-validation process. In the experiments, the best results were achieved by EfficientNetB0, achieving average accuracy and F1-Score of, respectively, 77.29% and 84.67%.

Paper Citation

in Harvard Style

Pessoa A., Quintanilha D., Dallyson Sousa de Almeida J., Braz Junior G., C. de Paiva A. and Cunha A. (2024). A Comparative Analysis of EfficientNet Architectures for Identifying Anomalies in Endoscopic Images. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 530-540. DOI: 10.5220/0012724900003690

in Bibtex Style

@conference{iceis24,
author={Alexandre Pessoa and Darlan Quintanilha and João Dallyson Sousa de Almeida and Geraldo Braz Junior and Anselmo C. de Paiva and António Cunha},
title={A Comparative Analysis of EfficientNet Architectures for Identifying Anomalies in Endoscopic Images},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={530-540},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012724900003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - A Comparative Analysis of EfficientNet Architectures for Identifying Anomalies in Endoscopic Images
SN - 978-989-758-692-7
AU - Pessoa A.
AU - Quintanilha D.
AU - Dallyson Sousa de Almeida J.
AU - Braz Junior G.
AU - C. de Paiva A.
AU - Cunha A.
PY - 2024
SP - 530
EP - 540
DO - 10.5220/0012724900003690
PB - SciTePress