Towards an interpretable model for automatic classification of endoscopy images
Torres Treviño, Luis
Navarro-López, Eva María
González-González, José Alberto
EditorsPichardo Lagunas, Obdulia
Martínez Seis, Bella
MetadataShow full item record
AbstractDeep 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.
CitationGarcía-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_24
DescriptionThis 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 Intelligence
Lecture Notes in Computer Science