Brieva, Jorge

Main Affiliation

Facultad de Ingeniería - CampCM

Preferred name

Brieva, Jorge

Official Name

Brieva Rico, Jorge Eduardo

ORCID

0000-0002-5430-8778

Researcher ID

GMW-951-2022

Scopus Author ID

7005997081

Web Site

https://www.up.edu.mx/investigacion/jorge-eduardo-brieva-rico/

Now showing 1 - 10 of 86

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Edge-Enhanced Knowledge Distillation System for Diabetic Retinopathy Lesions Computer-Aided Diagnosis
(Springer Nature Switzerland, 2025)
Lopez-Figueroa, Alberto
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Jacome-Herrera, Sebastian
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Moya-Albor, Ernesto
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Renza, Diego
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Brieva, Jorge
This work addresses the challenge of deploying computationally intensive Deep Learning (DL) models for Diabetic Retinopathy (DR) lesion detection in clinical settings, particularly on resource-constrained edge devices. DR is a significant global health issue and a leading cause of preventable blindness, making early and accessible detection crucial. We propose a proof-of-concept system utilizing Knowledge Distillation (KD) to create a tiny, efficient DL model for DR lesion detection, specifically designed for embedding into retinal scanners via the NVIDIA Jetson Nano platform. Our novel approach employs a KD framework where a pre-trained Inception-v3 model acts as the ‘teacher,’ fine-tuned on fundus image data. This teacher model distills its knowledge into a compact ‘student’ model based on the MobileNet-v2 architecture, which is trained on a small, synthetically generated dataset optimized through an iterative distillation process using a custom loss function combining Kullback-Leibler divergence and Categorical Cross-Entropy. This method significantly reduces model size and computational requirements while maintaining high diagnostic accuracy, comparable to larger, state-of-the-art models. By enabling real-time, on-device analysis, this embedded AI solution enhances data privacy, ensures consistent performance, and improves the accessibility of advanced DR screening, particularly in remote or underserved healthcare environments. This makes AI-assisted DR detection feasible for widespread clinical adoption directly within scanning devices. ©The authors ©Springer.
24 5
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Vision-Based Autonomous Navigation with Evolutionary Learning
(2020)
Coronel, Sandra L.
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Moya-Albor, Ernesto
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Ponce, Hiram
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Brieva, Jorge
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Chávez Domínguez, Rodrigo
In this paper, we propose a vision-based autonomous robotics navigation system, it uses a bio-inspired optical flow approach using the Hermite transform and a fuzzy logic controller, the input membership functions were tuned applying a distributed evolutionary learning based on social wound treatment inspired in the Megaponera analis ant. The proposed method was implemented in a virtual robotics system using the V-REP software and in communication con MATLAB. The results show that the optimization of the input fuzzy membership functions improves the navigation behavior against an empirical tuning of them. © 2020, Springer Nature Switzerland AG.
1 9Scopus© Citations 2
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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.
21
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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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Non-contact breathing rate monitoring system using a magnification technique and artificial hydrocarbon networks
(2020)
Brieva, Jorge
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Moya-Albor, Ernesto
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Ponce, Hiram
In this paper, we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion video magnification technique and an Artificial Hydrocarbon Networks (AHN) as classifier. After the magnification procedure, a AHN is trained to detect the inhalation and exhalation frames in the video. From this classification, the respiratory rate is estimated. The magnification procedure was carried out using the Hermite decomposition. The respiratory rate (RR) is estimated from the classified frames. We have tested the method on 10 healthy subjects in different positions. To compare performance of methods to respiratory rate the mean average error and a Bland and Altman analysis is used to investigate the agreement of the methods. The mean average error for our strategy is 4.46 ± 3.68% with and agreement with respect of the reference of ˜ 98 %. © 2020 SPIE
1 6
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Texture descriptor approaches to level set segmentation in medical images
(2014)
Olveres, Jimena
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Nava, Rodrigo
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Escalante-Ramírez, Boris
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Cristóbal, Gabriel
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Vallejo, Enrique
Medical image analysis has become an important tool for improving medical diagnosis and planning treatments. It involves volume or still image segmentation that plays a critical role in understanding image content by facilitating extraction of the anatomical organ or region-of-interest. It also may help towards the construction of reliable computer-aided diagnosis systems. Specifically, level set methods have emerged as a general framework for image segmentation; such methods are mainly based on gradient information and provide satisfactory results. However, the noise inherent to images and the lack of contrast information between adjacent regions hamper the performance of the algorithms, thus, others proposals have been suggested in the literature. For instance, characterization of regions as statistical parametric models to handle level set evolution. In this paper, we study the influence of texture on a level-set-based segmentation and propose the use of Hermite features that are incorporated into the level set model to improve organ segmentation that may be useful for quantifying left ventricular blood flow. The proposal was also compared against other texture descriptors such as local binary patterns, Image derivatives, and Hounsfield low attenuation values
1 26Scopus© Citations 7
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An Intelligent Human Fall Detection System Using a Vision-Based Strategy
(2019)
Brieva, Jorge
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Ponce, Hiram
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Moya-Albor, Ernesto
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Martinez-Villaseñor, Lourdes
Elderly people is increasing dramatically during the current years, and it is expected that this population reaches 2.1 billion of individuals by 2050. In this regard, new care strategies are required. Assisted living technologies have proposed alternatives to support professional caregivers and families to take care of elderly people, such as in risk of falls. Currently, fall detection systems are able to alleviate the latter problem and reduce the time a person who suffered a fall receives assistance. Thus, this paper proposes a fall detection system based on image processing strategy to extract motion features through an optical flow method. For classification, we use these features as inputs to a convolutional neural network. We applied our approach in a dataset comprises video recordings of one subject performing different types of falls. In experimental results, our approach showed 92% accuracy on the dataset used. © 2019 IEEE.
1 29Scopus© Citations 9
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Non-contact breathing rate monitoring system based on a Hermite video magnification technique
(2018)
Brieva, Jorge
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Moya-Albor, Ernesto
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Yael Rivas Scott, Orlando
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Ponce, Hiram
In this paper we present a new non-contact strategy to estimate the breathing rate based on the Eulerian motion magnification technique and a system based on di€erent images processing steps. After the magnification procedure, a ROI is selected manually, an enhancement algorithm based on an adaptive histogram equalization is applied and finally the frames are binarized using the Otsu algorithm. Morphological operations are carry out on the video frames and a tracking temporal strategy is implemented to estimate the breathing rate. The magnification procedure was carried out using an Hermite decomposition. We have tested the method on three subjects in four positions (seat, lying face down, lying face up and lying in fetal position). The motion magnification approach is compared to the Laplacian decomposition strategy computing the mean absolute error. © SPIE. Downloading of the abstract is permitted for personal use only.
2 9Scopus© Citations 7
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Coronary artery segmentation in X-Ray Angiographic image by means of a shape based level set method
(2013)
Brieva, Jorge
This paper presents a level set technique to extract vascular structures in X-Ray Angiographic images. It makes uses of the Chan and Vese model applied to images of non-uniform illumination and uses a shape-based model to perform the segmentation. The shape model is computed using string matching techniques. Its performance, using different metrics, has been evaluated on a image sequence of 64 angiographic images by comparison with expert delineation. A sensitivity of 81% and a specificity of 94% were found in the quantitative validation analysis.
Scopus© Citations 3 2 33
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A novel artificial organic control system for mobile robot navigation in assisted living using vision- and neural-based strategies
(2018)
Ponce, Hiram
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Moya-Albor, Ernesto
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Brieva, Jorge
Robots in assisted living (RAL) are an alternative to support families and professional caregivers with a wide range of possibilities to take care of elderly people. Navigation of mobile robots is a challenging problem due to the uncertainty and dynamics of environments found in the context of places for elderly. To accomplish this goal, the navigation system tries to replicate such a complicated process inspired on the perception and judgment of human beings. In this work, we propose a novel nature-inspired control system for mobile RAL navigation using an artificial organic controller enhanced with vision-based strategies such as Hermite optical flow (OF) and convolutional neural networks (CNNs). Particularly, the Hermite OF is employed for obstacle motion detection while CNNs are occupied for obstacle distance estimation. We train the CNN using OF visual features guided by ultrasonic sensor-based measures in a 3D scenario. Our application is oriented to avoid mobile and fixed obstacles using a monocular camera in a simulated environment. For the experiments, we use the robot simulator V-REP, which is an integrated development environment into a distributed control architecture. Security and smoothness metrics as well as quantitative evaluation are computed and analyzed. Results showed that the proposed method works successfully in simulation conditions.
Scopus© Citations 7 1 11

Brieva, Jorge

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