Manuel Jesús Domínguez Morales

Profesor Sustituto Interino a Tiempo Completo

Universidad de Sevilla

Spain

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Manuel Jesús Domínguez Morales is a Profesor Sustituto Interino a Tiempo Completo at Universidad de Sevilla, Spain. His research focuses on applying machine learning and deep learning techniques to medical diagnostics, including projects on gait-based Parkinson’s detection and brain tumor identification using MRI. He has also contributed to studies on ECG analysis in athletes and has developed systems for disease screening and prediction related to intimate partner violence.

Manuel Jesús Domínguez Morales

Research Interests

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Articles (18)

Gait-Based Parkinson’s Disease Detection Using Recurrent Neural Networks for Wearable Systems

Classification of skin blemishes with cell phone images using deep learning techniques

Brain Tumor Detection Using Magnetic Resonance Imaging and Convolutional Neural Networks

Optimized Machine Learning Classifiers for Symptom-Based Disease Screening

Diagnosis Aid System for Colorectal Cancer Using Low Computational Cost Deep Learning Architectures

An innovative 12-lead resting electrocardiogram dataset in professional football

Analyzing ECG signals in professional football players using machine learning techniques

Energy efficiency in edge TPU vs. embedded GPU for computer-aided medical imaging segmentation and classification

A Robust Ensemble of Convolutional Neural Networks for the Detection of Monkeypox Disease from Skin Images

Using machine learning-based systems to help predict disengagement from the legal proceedings by women victims of intimate partner violence in Spain

Designing and evaluating a wearable device for affective state level classification using machine learning techniques

Wearable Health Devices for Diagnosis Support: Evolution and Future Tendencies

Special Issue “Fighting COVID-19: Emerging Techniques and Aid Systems for Prevention, Forecasting and Diagnosis”

Non-small cell lung cancer diagnosis aid with histopathological images using Explainable Deep Learning techniques

On the feature extraction process in machine learning. An experimental study about guided versus non-guided process in falling detection systems

Worker’s physical fatigue classification using neural networks

A study on the use of Edge TPUs for eye fundus image segmentation

A Deep-Learning Based Posture Detection System for Preventing Telework-Related Musculoskeletal Disorders

Collaborators (5)

José María Rodríguez Corral

María Jesús Cala Carrillo

Anton Civit

Arturo Morgado-Estevez

Rocío Bohórquez Gómez-Millán

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