Detection and risk assessment of COVID-19 through machine learning DOI Open Access
B. Luna-Benoso, J. C. Martínez-Perales,

J. Cortés-Galicia

et al.

International Journal of ADVANCED AND APPLIED SCIENCES, Journal Year: 2024, Volume and Issue: 11(1), P. 207 - 216

Published: Jan. 1, 2024

COVID-19, also known as coronavirus disease, is caused by the SARS-CoV-2 virus. People infected with COVID-19 may show a range of symptoms from mild to severe, including fever, cough, difficulty breathing, tiredness, and nasal congestion, among others. The goal this study use machine learning identify if person has based on their predict how severe illness might become. This could lead outcomes like needing ventilator or being admitted an Intensive Care Unit. methods used in research include Artificial Neural Networks (specifically, Multi-Layer Perceptrons), Classification Regression Trees, Random Forests. Data National Epidemiological Surveillance System Mexico City was analyzed. findings indicate that Perceptron model most accurate, 87.68% success rate. It best at correctly identifying cases. Forests were more effective predicting cases those requiring Unit admission, while Trees accurate patients who needed be put ventilator.

Language: Английский

Automated detection and forecasting of COVID-19 using deep learning techniques: A review DOI
Afshin Shoeibi, Marjane Khodatars, Mahboobeh Jafari

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: 577, P. 127317 - 127317

Published: Jan. 26, 2024

Language: Английский

Citations

56
Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture DOI Creative Commons
Md. Alamin Talukder, Md. Abu Layek, Mohsin Kazi

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 168, P. 107789 - 107789

Published: Nov. 30, 2023

The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across planet. It is a highly contagious respiratory disease requiring early accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying genetic composition coronavirus, exhibiting relatively low rate time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as valuable approach within diagnostic protocol. This study investigates potential leveraging radiographic imaging (X-rays) with deep learning algorithms swiftly precisely identify patients. proposed elevates accuracy by fine-tuning appropriate layers on various established transfer models. experimentation was conducted X-ray dataset containing 2000 images. rates achieved were impressive 99.55%, 97.32%, 99.11%, 99.11% 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 EfficientNetB4 respectively. fine-tuned an excellent score, showcasing robust model. Furthermore, excelled in Lung Chest 4,350 Images, achieving remarkable performance 99.17%, precision 99.13%, recall 99.16%, f1-score 99.14%. These results highlight promise efficient lung through medical imaging, especially research offers radiologists effective means aiding precise diagnosis contributes assistance healthcare professionals accurately affected

Language: Английский

Citations

44
Efficient pneumonia detection using Vision Transformers on chest X-rays DOI Creative Commons
Sukhendra Singh, Manoj Kumar, Abhay Kumar

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 30, 2024

Abstract Pneumonia is a widespread and acute respiratory infection that impacts people of all ages. Early detection treatment pneumonia are essential for avoiding complications enhancing clinical results. We can reduce mortality, improve healthcare efficiency, contribute to the global battle against disease has plagued humanity centuries by devising deploying effective methods. Detecting not only medical necessity but also humanitarian imperative technological frontier. Chest X-rays frequently used imaging modality diagnosing pneumonia. This paper examines in detail cutting-edge method detecting implemented on Vision Transformer (ViT) architecture public dataset chest available Kaggle. To acquire context spatial relationships from X-ray images, proposed framework deploys ViT model, which integrates self-attention mechanisms transformer architecture. According our experimentation with Transformer-based framework, it achieves higher accuracy 97.61%, sensitivity 95%, specificity 98% X-rays. The model preferable capturing context, comprehending relationships, processing images have different resolutions. establishes its efficacy as robust solution surpassing convolutional neural network (CNN) based architectures.

Language: Английский

Citations

24
Detection of bruises on red apples using deep learning models DOI
Zeynep Ünal, Тефиде Кизилдениз, Mustafa ÖZDEN

et al.

Scientia Horticulturae, Journal Year: 2024, Volume and Issue: 329, P. 113021 - 113021

Published: Feb. 27, 2024

Language: Английский

Citations

20
Chest X-ray Images for Lung Disease Detection Using Deep Learning Techniques: A Comprehensive Survey DOI
Mohammed A. A. Al‐qaness,

Jie Zhu,

Dalal AL-Alimi

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: 31(6), P. 3267 - 3301

Published: Feb. 19, 2024

Language: Английский

Citations

12
Role of Artificial Intelligence in Medical Image Analysis: A Review of Current Trends and Future Directions DOI
Xin Li, Lei Zhang, Jingsi Yang

et al.

Journal of Medical and Biological Engineering, Journal Year: 2024, Volume and Issue: 44(2), P. 231 - 243

Published: April 1, 2024

Language: Английский

Citations

12
Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds DOI Creative Commons
Hassaan Malik, Tayyaba Anees

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(3), P. e0296352 - e0296352

Published: March 12, 2024

Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by overlapping symptoms (such fever, cough, sore throat, etc.). Additionally, researchers make use X-rays (CXR), cough sounds, computed tomography (CT) scans diagnose disorders. The present study aims classify nine different disorders, including LC, COL, atelectasis (ATE), tuberculosis (TB), pneumothorax (PNEUTH), edema (EDE), pneumonia (PNEU). Thus, we suggested four novel convolutional neural network (CNN) models that train distinct image-level representations for classifications extracting features from images. Furthermore, proposed CNN employed several new approaches max-pooling layer, batch normalization layers (BANL), dropout, rank-based average pooling (RBAP), multiple-way data generation (MWDG). scalogram method is utilized transform sounds coughing into visual representation. Before beginning model has been developed, SMOTE approach used calibrate CXR CT well sound images (CSI) CXR, scan, CSI training evaluating come 24 publicly available benchmark illness datasets. classification performance compared with seven baseline models, namely Vgg-19, ResNet-101, ResNet-50, DenseNet-121, EfficientNetB0, DenseNet-201, Inception-V3, in addition state-of-the-art (SOTA) classifiers. effectiveness further demonstrated results ablation experiments. was successful achieving an accuracy 99.01%, making it superior both SOTA As result, capable offering significant support radiologists other professionals.

Language: Английский

Citations

10
Reinforced Collaborative-Competitive Representation for Biomedical Image Recognition DOI
Junwei Jin, S. Kevin Zhou, Yanting Li

et al.

Interdisciplinary Sciences Computational Life Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

Language: Английский

Citations

1
An Analysis of Deep Transfer Learning-Based Approaches for Prediction and Prognosis of Multiple Respiratory Diseases Using Pulmonary Images DOI
Apeksha Koul, Rajesh K. Bawa, Yogesh Kumar

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 31(2), P. 1023 - 1049

Published: Oct. 31, 2023

Language: Английский

Citations

18
Artificial Intelligence in battling infectious diseases: A transformative role DOI
Chunhui Li,

Guoguo Ye,

Yinghan Jiang

et al.

Journal of Medical Virology, Journal Year: 2024, Volume and Issue: 96(1)

Published: Jan. 1, 2024

Abstract It is widely acknowledged that infectious diseases have wrought immense havoc on human society, being regarded as adversaries from which humanity cannot elude. In recent years, the advancement of Artificial Intelligence (AI) technology has ushered in a revolutionary era realm disease prevention and control. This evolution encompasses early warning outbreaks, contact tracing, infection diagnosis, drug discovery, facilitation design, alongside other facets epidemic management. article presents an overview utilization AI systems field diseases, with specific focus their role during COVID‐19 pandemic. The also highlights contemporary challenges confronts within this domain posits strategies for mitigation. There exists imperative to further harness potential applications across multiple domains augment its capacity effectively addressing future outbreaks.

Language: Английский

Citations

8