Gutiérrez, SebastiánSebastiánGutiérrezCruz-Aceves, IvanIvanCruz-AcevesFernandez-Jaramillo, Arturo AlfonsoArturo AlfonsoFernandez-JaramilloMoya-Albor, ErnestoErnestoMoya-AlborBrieva, JorgeJorgeBrievaPonce, HiramHiramPonce2023-08-012023-08-012022Rendon-Aguilar, L. D., Cruz-Aceves, I., Fernandez‐Jaramillo, A. A., Moya-Albor, E., Brieva, J., & Ponce, H. (2022). Automatic Classification of Coronary Stenosis using Convolutional Neural Networks and Simulated Annealing. En CRC Press eBooks (pp. 227-247). https://doi.org/10.1201/9781003215141-11978-100321514-1978-103210400-3https://scripta.up.edu.mx/handle/20.500.12552/459410.1201/9781003215141-11Convolutional Neural Networks for Medical Image Processing ApplicationsAutomatic detection of coronary stenosis plays an essential role in systems that perform computer-aided diagnosis in cardiology. Coronary stenosis is a narrowing of the coronary arteries caused by plaque that reduces the blood flow to the heart. Automatic classification of coronary stenosis images has been re-cently addressed using deep and machine learning techniques. Generally, the machine learning methods form a bank of empirical and automatic features from the angiographic images. In the present work, a novel method for the automatic classification of coronary stenosis X-ray images is presented. The method is based on convolutional neural networks, where the neural architecture search is performed by using the path-based metaheuristics of simulated annealing. To perform the neural architecture search, the maximization of the F1-score metric is used as the fitness function. The automatically generated convolutional neural network was compared with three deep learning methods in terms of the accuracy and F1-score metrics using a testing set of images obtaining 0.88 and 0.89, respectively. In addition, the proposed method was evaluated with different sets of coronary stenosis images obtained via data augmentation. The results involving a number of different instances have shown that the proposed architecture is robust preserving the efficiency with different datasets © 2023 Şaban öztürk. All rights reserved.enDeep learningDiagnostic imagingImage interpretationComputer-assisted methodsImage processing --Computer-assisted methodsNeural networksComputer neural networks (Neurobiology)Resource Types::text::book::book part