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Author: search.filters.author.Franco-Gaona, Erick

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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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    Optimal Dataset Size for Fine-Tuning sEMG-Based Hand Gesture Recognition in Rehabilitation Prosthesis
    (IEEE, 2024)
    Escobedo-Gordillo, Andrés
    ;
    Brieva, Jorge  
    ;
    Moya-Albor, Ernesto  
    ;
    Ponce, Hiram  
    ;
    Franco-Gaona, Erick
    Surface electromyography (sEMG) has become a vital tool for controlling prostheses and rehabilitation using hand gesture recognition. However, the process of fine-tuning machine learning models to individual users often requires considerable amounts of data, which can be challenging to obtain due to user fatigue and discomfort. This work investigates the optimal dataset size needed for fine-tuning a pretrained Convolutional Neural Network (CNN) model for hand gesture recognition, using the NinaPro DB2 dataset. Our results show that training on just a third of the dataset achieves over 90% accuracy, highlighting a significant reduction in the data requirements compared to traditional methods. This approach can minimize the burden of data collection on users, making sEMG-based rehabilitation devices more practical and accessible. ©The authors ©IEEE
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