A5028149208- AI in cancer detection
- COVID-19 diagnosis using AI
- Speech Recognition and Synthesis
- Radiomics and Machine Learning in Medical Imaging
- Voice and Speech Disorders
- Context-Aware Activity Recognition Systems
- Technology Use by Older Adults
- Mobile Health and mHealth Applications
- Speech and Audio Processing
- EEG and Brain-Computer Interfaces
- Cutaneous Melanoma Detection and Management
- Stroke Rehabilitation and Recovery
- Artificial Intelligence in Healthcare
- ECG Monitoring and Analysis
- IoT and Edge/Fog Computing
- Attention Deficit Hyperactivity Disorder
- Autism Spectrum Disorder Research
- Sentiment Analysis and Opinion Mining
- Diabetic Foot Ulcer Assessment and Management
- Phonetics and Phonology Research
- Nutrition and Health in Aging
- Digital Imaging for Blood Diseases
- Pressure Ulcer Prevention and Management
- Colorectal Cancer Screening and Detection
- Non-Invasive Vital Sign Monitoring
Universidad de Deusto
2016-2025
Deleted Institution
2023
Applied Sciences (United States)
2023
Ente Vasco de la Energía
2017
University of Louisville
2017
Sciences and Education Research Council
2013
This article presents a review of the methods used in recognition and analysis human gait from three different approaches: image processing, floor sensors placed on body. Progress new technologies has led development series devices techniques which allow for objective evaluation, making measurements more efficient effective providing specialists with reliable information. Firstly, an introduction key parameters semi-subjective is presented. Secondly, studies are reviewed. Finally, based...
Sometimes, one needs to control different emotional situations which can lead the person suffering them dangerous situations, in both medium and short term. There are studies indicate that stress increases risk of cardiac problems. In this study we have designed built a sensor based on Galvanic Skin Response (GSR), controlled by ZigBee. order check device's performance, used 16 adults (eight women eight men) who completed tests requiring certain degree effort, such as mathematical operations...
Breast cancer is one of the major public health issues and considered a leading cause cancer-related deaths among women worldwide. Its early diagnosis can effectively help in increasing chances survival rate. To this end, biopsy usually followed as gold standard approach which tissues are collected for microscopic analysis. However, histopathological analysis breast non-trivial, labor-intensive, may lead to high degree disagreement pathologists. Therefore, an automatic diagnostic system...
This study presents a computationally efficient deep learning model for binary sentiment classification, which aims to decide the polarity of people's opinions, attitudes, and emotions expressed in written text. To achieve this, we exploited three widely practiced datasets based on public opinions about movies. We utilized merely one bidirectional long short-term memory (BiLSTM) layer along with global pooling mechanism achieved an accuracy 80.500%, 85.780%, 90.585% MR, SST2 IMDb datasets,...
Nowadays, coronavirus (COVID-19) is getting international attention due it considered as a life-threatened epidemic disease that hard to control the spread of infection around world. Machine learning (ML) one intelligent technique able automatically predict event with reasonable accuracy based on experience and process. In meantime, rapid number ML models have been proposed for predicate cases COVID-19. Thus, there need an evaluation benchmarking COVID-19 which main challenge this study....
Melanoma has become more widespread over the past 30 years and early detection is a major factor in reducing mortality rates associated with this type of skin cancer. Therefore, having access to an automatic, reliable system that able detect presence melanoma via dermatoscopic image lesions and/or pigmentation can be very useful tool area medical diagnosis.Among state-of-the-art methods used for automated or computer assisted diagnosis, attention should drawn Deep Learning based on...
The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. timely diagnosis infected patients is a critical step to limit spread COVID-19 epidemic. chest radiography imaging has shown be an effective screening technique in diagnosing To reduce radiologists control epidemic, fast accurate hybrid deep learning framework for virus X-ray images developed termed as COVID-CheXNet system. First, contrast image was enhanced noise level...
Voice pathology disorders can be effectively detected using computer-aided voice classification tools. These tools diagnose pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction detection tool based on Deep Learning. In this paper, pre-trained Convolutional Neural Network (CNN) was applied dataset of maximize the accuracy. also proposes distinguished training method combined with various strategies in order generalize...
The most commonly injured ligament in the human body is an anterior cruciate (ACL). ACL injury standard among football, basketball and soccer players. study aims to detect early stage via efficient thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. proposed approach this paper used customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing data...
Breast cancer analysis implies that radiologists inspect mammograms to detect suspicious breast lesions and identify mass tumors. Artificial intelligence techniques offer automatic systems for segmentation assist in their diagnosis. With the rapid development of deep learning its application medical imaging challenges, UNet variations is one state-of-the-art models image showed promising performance on mammography. In this paper, we propose an architecture, called Connected-UNets, which...
Coronavirus (COVID-19) epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, no clinically approved vaccine or antiviral medicine currently available. Early diagnosis of infected patients through effective screening needed to control the rapid spread this virus. Chest radiography imaging an tool for COVID-19 follow-up. Here, a novel hybrid multimodal deep learning system identifying in chest X-ray...
A compact textile ultra-wideband (UWB) antenna with an electrical dimension of 0.24λo × 0.009λo microstrip line feed at lower edge and a frequency operation 2.96 GHz is proposed for UWB application. The analytical investigation using circuit theory concepts the cavity model presented to validate design. main contribution this paper propose wearable wide impedance bandwidth 118.68 % (2.96-11.6 GHz) applicable range 3.1 10.6 GHz. results present maximum gain 5.47 dBi 7.3 frequency. Moreover,...
Heart diseases are highly ranked among the leading causes of mortality in world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number medical features reported for patients Electronic Health Records (EHR) that allow physicians to diagnose monitor We collected a dataset from Medica Norte Hospital Mexico includes 800 records 141 indicators such as age, weight, glucose, blood pressure rate, clinical symptoms. Distribution is very unbalanced on...
The quick spread of the Coronavirus Disease (COVID-19) infection around world considered a real danger for global health. biological structure and symptoms COVID-19 are similar to other viral chest maladies, which makes it challenging big issue improve approaches efficient identification disease. In this study, an automatic prediction is proposed automatically discriminate between healthy infected subjects in X-ray images using two successful moderns traditional machine learning methods...
This paper presents a real-time air quality monitoring system based on Internet of Things. Air is particularly relevant for enhanced living environments and well-being. The Environmental Protection Agency the World Health Organization have acknowledged material impact public health defined standards policies to regulate improve quality. However, there significant need cost-effective methods monitor control which provide modularity, scalability, portability, easy installation configuration...
Currently, most mask extraction techniques are based on convolutional neural networks (CNNs). However, there still numerous problems that need to solve. Thus, the advanced methods deploy artificial intelligence (AI) necessary. The use of cooperative agents in increases efficiency automatic image segmentation. Hence, we introduce a new method is multi-agent deep reinforcement learning (DRL) minimize long-term manual and enhance medical segmentation frameworks. A DRL-based introduced deal with...
Abstract Breast cancer is a common malignancy and leading cause of cancer-related deaths in women worldwide. Its early diagnosis can significantly reduce the morbidity mortality rates women. To this end, histopathological usually followed as gold standard approach. However, process tedious, labor-intensive, may be subject to inter-reader variability. Accordingly, an automatic diagnostic system assist improve quality diagnosis. This paper presents deep learning approach automatically classify...