Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis
Journal
2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM)
Publisher
IEEE
Date Issued
2024
Author(s)
Franco-Gaona, Erick
Avila-Garcia, Maria-Susana
Cruz-Aceves, Ivan
Orocio-Garcia, Hiram-Efrain
Escobedo-Gordillo, Andrés
Type
Resource Types::text::conference output::conference proceedings
Abstract
Coronary stenosis is a disease that claims millions of lives each year. Early detection of this condition is crucial for patient survival. Currently, physicians perform detection by x-ray angiograms, however, the variability of diagnoses and the difficulty of access to expertise has led to the need for automated, computer-assisted diagnosis. In this work explores the use of deep learning to classify stenosis or non-stenosis in angiogram images using convolutional neural networks from scratch. A methodology to fine-tuning network architectures automatically using metaheuristic optimization techniques is proposed, demonstrating superior performance to fine-tuning empirically and proposing a new architecture in the literature to classify coronary stenosis. The proposed architectures achieved 86.02% and 95.67% F1-score with simulated annealing and iterated local search techniques, respectively. ©The authors ©IEEE
License
Acceso Restringido
How to cite
Franco-Gaona, E., Avila-Garcia, M.-S., Cruz-Aceves, I., Orocio-Garcia, H.-E., Escobedo-Gordillo, A., & Brieva, J. (2024). Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis. In 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM) (pp. 1–4). 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM). IEEE. https://doi.org/10.1109/sipaim62974.2024.10783513
Table of contents
I. Introduction -- II. Convolutional Neural Networks -- III. Neural Architecture Search -- IV. Results and Discussion.