Towards an interpretable model for automatic classification of endoscopy images
Authors
García-Aguirre, RogelioTorres Treviño, Luis
Navarro-López, Eva María

González-González, José Alberto
Editors
Pichardo Lagunas, ObduliaMartínez Seis, Bella
Martínez-Miranda, Juan
Issue Date
2022-10-23
Metadata
Show full item recordAbstract
Deep learning strategies have become the mainstream for computer-assisted diagnosis tools development since they outperform other machine learning techniques. However, these systems can not reach their full potential since the lack of understanding of their operation and questionable generalizability provokes mistrust from the users, limiting their application. In this paper, we generate a Convolutional Neural Network (CNN) using a genetic algorithm for hyperparameter optimization. Our CNN has state-of-the-art classification performance, delivering higher evaluation metrics than other recent papers that use AI models to classify images from the same dataset. We provide visual explanations of the classifications made by our model implementing Grad-CAM and analyze the behavior of our model on misclassifications using this technique.Citation
García-Aguirre, R., Torres Treviño, L., Navarro-López, E.M. and González-González, J.A. (2022) Towards an interpretable model for automatic classification of endoscopy images. Lecture Notes in Artificial Intelligence, 13612, pp. 297-307. DOI: 10.1007/978-3-031-19493-1_24Publisher
SpringerAdditional Links
https://link.springer.com/book/9783031194948Type
Conference contributionLanguage
enDescription
This is an accepted manuscript of a paper published by Springer in Lecture Notes in Artificial Intelligence on 23 October 2022. Conference paper presented at the 21st Mexican International Conference on Artificial Intelligence, 24th-29th October 2022. For re-use please see the publisher's terms and conditions.Series/Report no.
Lecture Notes in Artificial IntelligenceLecture Notes in Computer Science
ISSN
0302-9743EISSN
1611-3349ISBN
9783031194948ae974a485f413a2113503eed53cd6c53
10.1007/978-3-031-19493-1_24