A5011354002- Brain Tumor Detection and Classification
- Medical Image Segmentation Techniques
- Blind Source Separation Techniques
- Functional Brain Connectivity Studies
- Neural Networks and Applications
- Spectroscopy and Chemometric Analyses
- Image Retrieval and Classification Techniques
- Parkinson's Disease Mechanisms and Treatments
- Speech and Audio Processing
- EEG and Brain-Computer Interfaces
- AI in cancer detection
- COVID-19 diagnosis using AI
- Neurological disorders and treatments
- Dementia and Cognitive Impairment Research
- Face and Expression Recognition
- Medical Imaging Techniques and Applications
- Advanced Adaptive Filtering Techniques
- Gene expression and cancer classification
- Speech Recognition and Synthesis
- Radiomics and Machine Learning in Medical Imaging
- Alzheimer's disease research and treatments
- Neural dynamics and brain function
- Anomaly Detection Techniques and Applications
- Music and Audio Processing
- Machine Learning in Healthcare
Universidad de Granada
2016-2025
Instituto Andaluz de Ciencias de la Tierra
2018-2025
Ludwig-Maximilians-Universität München
2022-2024
University of Cambridge
2017-2024
Munich Cluster for Systems Neurology
2024
University of Alicante
2002-2021
Deakin University
2020
Ferdowsi University of Mashhad
2020
Iran University of Medical Sciences
2020
K.N.Toosi University of Technology
2020
Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer's Disease (AD), which, in turn, allows application treatments that can be simpler and more likely to effective. This paper explores construction classification methods based on deep learning architectures applied brain regions defined by Automated Anatomical Labeling (AAL). Gray Matter (GM) images from each area have been split into 3D patches according AAL atlas these are used train different...
Integrating artificial intelligence with food category recognition has been a field of interest for research the past few decades. It is potentially one next steps in revolutionizing human interaction food. The modern advent big data and development data-oriented fields like deep learning have provided advancements recognition. With increasing computational power ever-larger datasets, approach's potential yet to be realized. This survey provides an overview methods that can applied various...
Many classical machine learning techniques have been used to explore Alzheimer's disease (AD), evolving from image decomposition such as principal component analysis toward higher complexity, non-linear algorithms. With the arrival of deep paradigm, it has become possible extract high-level abstract features directly MRI images that internally describe distribution data in low-dimensional manifolds. In this work, we try a new exploratory AD based on convolutional autoencoders. We aim at...
COVID-19 pneumonia started in December 2019 and caused large casualties huge economic losses. In this study, we intended to develop a computer-aided diagnosis system based on artificial intelligence automatically identify the chest computed tomography images. We utilized transfer learning obtain image-level representation (ILR) backbone deep convolutional neural network. Then, novel neighboring aware (NAR) was proposed exploit relationships between ILR vectors. To information feature space...
The first known case of Coronavirus disease 2019 (COVID-19) was identified in December 2019. It has spread worldwide, leading to an ongoing pandemic, imposed restrictions and costs many countries. Predicting the number new cases deaths during this period can be a useful step predicting facilities required future. purpose study is predict rate one, three seven-day ahead next 100 days. motivation for every n days (instead just day) investigation possibility computational cost reduction still...
Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in brain, function some brain regions out balance, leading lack coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis via electroencephalography (EEG) signals. The obtained results are compared with those conventional methods. To implement proposed methods, dataset Institute Psychiatry Neurology Warsaw,...
Deep Learning (DL), a groundbreaking branch of Machine (ML), has emerged as driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted complex non-linear artificial neural systems, excel at extracting high-level features from data. demonstrated human-level performance real-world tasks, including clinical diagnostics, unlocked solutions to previously intractable problems virtual agent design, robotics, genomics, neuroimaging, computer vision, industrial...
Abstract Objective. Myocarditis poses a significant health risk, often precipitated by viral infections like coronavirus disease, and can lead to fatal cardiac complications. As less invasive alternative the standard diagnostic practice of endomyocardial biopsy, which is highly thus limited severe cases, magnetic resonance (CMR) imaging offers promising solution for detecting myocardial abnormalities. Approach. This study introduces deep model called ELRL-MD that combines ensemble learning...
This paper presents a novel computer-aided diagnosis (CAD) technique for the early of Alzheimer's disease (AD) based on nonnegative matrix factorization (NMF) and support vector machines (SVM) with bounds confidence. The CAD tool is designed study classification functional brain images. For this purpose, two different image databases are selected: single photon emission computed tomography (SPECT) database positron (PET) images, both them containing data patients healthy controls as...