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Preface : Machine Learning Methods in Biomedical Field Computer-Aided Diagnostics, Healthcare and Biology Applications
(Springer Science and Business Media Deutschland GmbH, 2026)
Moya-Albor, Ernesto
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Ponce, Hiram
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Brieva, Jorge
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Gomez-Coronel, Sandra L.
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Renza Torres, Diego
This book presents a multidisciplinary collection of machine learning approaches applied to the biomedical field, with a focus on computer-aided diagnostic systems, healthcare support tools, biological applications, and sustainable development in health. Computer-aided diagnostic systems leverage machine learning methods to support medical diagnosis, while healthcare support tools, biological applications, and sustainability-oriented studies aim to improve patients’ quality of life, propose innovative solutions to biological challenges, and incorporate sustainability into healthcare processes. The contributions in this book offer readers a holistic view of new methods used to address current biomedical challenges in medicine, biology, and health sciences. By applying artificial intelligence algorithms, mathematical theories, and emergent systems, these works demonstrate how such approaches can improve specific problems or propose innovative solutions. This book is valuable for readers interested in recent advances in machine learning for diagnostic systems, healthcare applications, biological research, and sustainability-related issues. ©The authors ©Springer.
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Design of an Underwater Mechatronics System for Detecting Dissolved Solids in Water
(IEEE, 2024)
Estudillo, Eduardo
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Bautista, Luis
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Cardenas, Gerardo
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Mendoza, Aura
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Perez, Francisco
Marine pollution has been one of the most important problems we need to address in modern days, as it can cause an imbalance in the marine life of a body of water. This work presents a proof-of-concept of an underwater unmanned autonomous vehicle for detecting dissolved solids. The system was designed based on the mechatronics engineering model, so that it can move around a body water while measures the solid particles in water. The preliminary results are promising, and we anticipate the use of this low-cost system to help measuring water pollution in near city body waters. ©The authors ©IEEE.
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Non-Contact Respiratory Rate Estimation in Newborns During Quiet Sleep Using Video Magnification Techniques and a 3D Convolutional Neural Network
(IEEE, 2024)
Escobedo Gordillo, Andrés Emiliano
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Rivas-Scott, Orlando Yael
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Brieva, Jorge
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Moya-Albor, Ernesto
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Cabon, Sandie
In this paper, we present a new non-contact strategy to estimate the respiratory rate (RR) in a neonatal intensive care unit (NICU) based on the Eulerian motion video magnification technique and a 3D Convolutional Neural Network (3D CNN). The magnification procedure was carried out using the Hermite decomposition. The RR is estimated using a 3D CNN and a region of interest (ROI) detected manually. We have tested the method on 8 infants in NICU during quiet sleep. A contact respiratory signal is acquired synchronously to the videos to compute the RR as reference for training the CNN. To compare the performance of the method, we compute the Mean Absolute Error, the Root Mean Squared Error and metrics from the Bland and Altman analysis to investigate the agreement of the method with respect to the respiratory signal reference. The proposed solution shows an agreement with respect to the reference of 95% and root mean squared error of 2.88. ©The authors ©IEEE.
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Neural Architecture Search Using Trajectory Metaheuristics to Classify Coronary Stenosis
(IEEE, 2024)
Franco-Gaona, Erick
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Avila-Garcia, Maria-Susana
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Cruz-Aceves, Ivan
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Orocio-Garcia, Hiram-Efrain
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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
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Brieva, Jorge
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Moya-Albor, Ernesto
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Ponce, Hiram
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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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Data-Driven Innovation for Intelligent Technology : Perspectives and Applications in ICT
(Springer Cham, 2024)
Ponce, Hiram
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Brieva, Jorge
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Lozada Flores, Octavio
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Martinez-Villaseñor, Lourdes
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Moya-Albor, Ernesto
This book focuses on new perspectives and applications of data-driven innovation technologies, applied artificial intelligence, applied machine learning and deep learning, data science, and topics related to transforming data into value. It includes theory and use cases to help readers understand the basics of data-driven innovation and to highlight the applicability of the technologies. It emphasizes how the data lifecycle is applied in current technologies in different business domains and industries, such as advanced materials, healthcare and medicine, resource optimization, control and automation, among others. This book is useful for anyone interested in data-driven innovation for smart technologies, as well as those curious in implementing cutting-edge technologies to solve impactful artificial intelligence, data science, and related information technology and communication problems. ©Springer. ©The authors. ©The editors
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A Vision-based Robotic Navigation Method Using an Evolutionary and Fuzzy Q-Learning Approach
(Intelligence Science and Technology Press, 2024)
Cuesta-Solano, Roberto
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Moya-Albor, Ernesto
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Ponce, Hiram
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Brieva, Jorge
The paper presents a fuzzy Q-learning (FQL) and optical flow-based autonomous navigation approach. The FQL method takes decisions in an unknown environment and without mapping, using motion information and through a reinforcement signal into an evolutionary algorithm. The reinforcement signal is calculated by estimating the optical flow densities in areas of the camera to determine whether they are “dense” or “thin” which has a relationship with the proximity of objects. The results obtained show that the present approach improves the rate of learning compared with a method with a simple reward system and without the evolutionary component. The proposed system was implemented in a virtual robotics system using the CoppeliaSim software and in communication with Python. ©The authors ©INTELLIGENCE SCIENCE AND TECHNOLOGY PRESS INC.
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Computer-Aided Diagnosis of Diabetic Retinopathy Lesions Based on Knowledge Distillation in Fundus Images
(2024)
Moya-Albor, Ernesto
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Alberto Leandro Figueroa Soliz
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Sebastian Herrera Uribe
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Diego Renza
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Brieva, Jorge
<jats:p>At present, the early diagnosis of diabetic retinopathy (DR), a possible complication of diabetes due to elevated glucose concentrations in the blood, is usually performed by specialists using a manual inspection of high-resolution fundus images based on lesion screening, leading to problems such as high work-intensity and accessibility only in specialized health centers. To support the diagnosis of DR, we propose a deep learning-based (DL) DR lesion classification method through a knowledge distillation (KD) strategy. First, we use the pre-trained DL architecture, Inception-v3, as a teacher model to distill the dataset. Then, a student model, also using the Inception-v3 model, is trained on the distilled dataset to match the performance of the teacher model. In addition, a new combination of Kullback–Leibler (KL) divergence and categorical cross-entropy (CCE) loss is used to measure the difference between the teacher and student models. This combined metric encourages the student model to mimic the predictions of the teacher model. Finally, the trained student model is evaluated on a validation dataset to assess its performance and compare it with both the teacher model and another competitive DL model. Experiments are conducted on the two datasets, corresponding to an imbalanced and a balanced dataset. Two baseline models (Inception-v3 and YOLOv8) are evaluated for reference, obtaining a maximum training accuracy of 66.75% and 90.90%, respectively, and a maximum validation accuracy of 35.94% and 81.52%, both for the imbalanced dataset. On the other hand, the proposed DR classification model achieves an average training accuracy of 99.01% and an average validation accuracy of 97.30%, overcoming the baseline models and other state-of-the-art works. Experimental results show that the proposed model achieves competitive results in DR lesion detection and classification tasks, assisting in the early diagnosis of diabetic retinopathy.</jats:p>
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Contactless Video-Based Vital-Sign Measurement Methods: A Data-Driven Review
(Springer, 2024-01-01)
Brieva, Jorge
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Escobedo-Gordillo, Andrés
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Moya-Albor, Ernesto
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Ponce, Hiram
Nowadays, the healthcare is a priority for both governments and persons. Vital sign monitoring allows knowing the health status and is widely used for prevention, diagnosis, and treatment of determined illnesses. In particular, breathing and heart rate are traditionally considered the most relevant and accessible vital signs. However, oxygen saturation was essential in the COVID-19 pandemic. On the other hand, contact techniques to estimate these vital signs are a standard monitoring reference. However, non-contact estimation methods have gained relevance in the last few years in those cases where there is the possibility of suffering stress, pain, and skin irritation in specific situations, as in the case of vulnerable skin in burn patients and neonates. In this chapter, a review of contactless video-based vital-sign methods is presented. The selected methods have a data-driven approach as an alternative when there is not theoretical model of the physiological phenomenon. Finally, a new framework with a general data-driven approach to estimate the most used vital signs is proposed. This framework includes a region of interest extraction stage, a video magnification technique to reveals subtle changes, and a machine learning method to estimate the vital signs. In addition, each step describes some recommendations and best practices found ©Springer.
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From Project-Based Learning to Innovative Technologies in Mechatronics Course: A Case Study in a Private University in Mexico City
(Springer, 2024-01-01)
Ponce, Hiram
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Moya-Albor, Ernesto
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Brieva, Jorge
Mechatronics engineering is a challenging discipline that needs different thinking and practices in contrast with traditional engineering. This challenging is mainly due to the demand of integration, collaboration and holistic approaches required during the design methodology. This study examines the transformation from traditional education to an in-deep professional and research focused projects. The key factor in the mechatronics learning practice includes the implementation of a major project focused on positive social impact solutions and the road map developed for this purpose. This work proposes a methodology that allows students develop a major project with a holistic view, including design constraints related to specific contextual aspects as economics, environmental, societal, ethical, health and sustainability. Also, students are able to develop professional and research skills. The methodology also allows students propose a major project focused on positive social impact with design constraints. It also exposes students different engineering and computational tools for collaboration and integration. The study uses data from 69 students enrolled along four years, from 2016 to 2019. Results show that the student learning outcomes increased significantly at the end of the period time, from to (in range between 0 to 4), reaching the satisfactory level (year-2016 as baseline). Also, 100% of the scientific papers derived from the major projects were accepted for publication in international conferences © 2024 Springer Nature
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