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Author: search.filters.author.Avila-Garcia, Maria-SusanaSubject: search.filters.subject.Metaheuristics

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    Item type:Publication,
    Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis
    (IEEE, 2024)
    Franco-Gaona, Erick
    ;
    Avila-Garcia, Maria-Susana
    ;
    Cruz-Aceves, Ivan
    ;
    Orocio-Garcia, Hiram-Efrain
    ;
    Escobedo-Gordillo, Andrés
    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
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