https://rmib.rmib.com.mx/index.php/rmib <center> <p><strong>MISSION</strong></p> <p align="left"><em>La Revista Mexicana de Ingeniería Biomédica</em> (The Mexican Journal of Biomedical Engineering, RMIB, for its Spanish acronym) is a publication oriented to the dissemination of papers of the Mexican and international scientific community whose lines of research are aligned to the improvement of the quality of life through engineering techniques.</p> <p align="left">The papers that are considered for being published in the RMIB must be original, unpublished, and first rate, and they can cover the areas of Medical Instrumentation, Biomedical Signals, Medical Information Technology, Biomaterials, Clinical Engineering, Physiological Models, and Medical Imaging as well as lines of research related to various branches of engineering applied to the health sciences.</p> <p align="left">The RMIB is an electronic publication continuously released since 2020, structured into three volumes (January, May, September) by the Mexican Society of Biomedical Engineering, founded since 1979. It publishes articles in spanish and english and is aimed at academics, researchers and professionals interested in the subspecialties of Biomedical Engineering.</p> <p><strong>INDEXES</strong></p> <p><em>La Revista Mexicana de Ingeniería Biomédica</em> is a quarterly publication, and it is found in the following indexes:</p> <p><img src="https://www.rmib.mx/public/site/images/administrador/índices_y_repositorios_(1100_×_1000 px).jpg" /></p> </center> Sociedad Mexicana de Ingeniería Biomédica A.C. en-US Revista Mexicana de Ingenieria Biomedica 0188-9532 <p>Upon acceptance of an article in the RMIB, corresponding authors will be asked to fulfill and sign the copyright and the journal publishing agreement, which will allow the RMIB authorization to publish this document in any media without limitations and without any cost. Authors may reuse parts of the paper in other documents and reproduce part or all of it for their personal use as long as a bibliographic reference is made to the RMIB. However written permission of the Publisher is required for resale or distribution outside the corresponding author institution and for all other derivative works, including compilations and translations.</p> https://rmib.rmib.com.mx/index.php/rmib/article/view/1526 <p>Video capsule endoscopy (VCE) is a noninvasive procedure for diagnosing small bowel (SB) lesions. During VCE procedures, the miniature camera captures thousands of images, requiring considerable time and effort from healthcare professionals to examine each image for abnormalities. This study aims to develop and evaluate a modified DenseNet-201 convolutional neural network (CNN) model to assist in the automatic classification of SB lesions in VCE images. A representative dataset of 5,899 images was created by merging two state-of-the-art datasets, including six types of lesions and one class without lesions. A synthetic oversampling approach generated 1,273 synthetic images for the underrepresented class. Synthetic image quality was evaluated using texture metrics. The experiment was conducted on a set of 7,172 images using data partitions of 70 %, 15 %, and 15 % for training, validation, and testing, respectively, with a Monte Carlo cross-validation to verify the consistency of the experimental results. The model achieved an accuracy of 89.78 %, a precision of 89.66 %, a sensitivity of 89.69 %, a specificity of 89.72 %, and an F1 score of 89.67 % in the test set. The modified DenseNet-201 CNN model could be a valuable tool for diagnosing SB conditions and improving patient outcomes.</p> Erik Orlando Cuevas Rodriguez Carlos Eric Galván Tejada Rafael Magallanes Quintanar Jorge Issac Galván Tejada José María Celaya Padilla Juan Rubén Delgado Contreras Copyright (c) 2025 Revista Mexicana de Ingenieria Biomedica https://creativecommons.org/licenses/by-nc/4.0/ 2025-09-21 2025-09-21 46 Special Issue e1526 e1526 10.17488/RMIB.46.SI-TAIH.1526