Comparing Convolutional Neural Networks for Covid-19 Detection in Chest X-Ray Images DOI
Neeraj Varshney, Parul Madan, Anurag Shrivastava

et al.

2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Journal Year: 2023, Volume and Issue: unknown, P. 1789 - 1795

Published: Dec. 1, 2023

The study has been conducted to understand the effectiveness of CNN in case "COVID-19" detection by using all X-ray images chest. This helps health sector facilitate medical performance and conduct analytical procedure ML. aim objective have properly mentioned first segment study. issues also identified second chapter previous work related methodology illustrated methods that applied implement project. resulting themes with proper evaluation a comprehensive way. last represented conclusion recommendations future can be implemented later.

Language: Английский

Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence DOI Creative Commons
Lubna Abdelkareim Gabralla, Ali Mohamed Hussien, Abdulaziz AlMohimeed

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 13(18), P. 2939 - 2939

Published: Sept. 13, 2023

Colon cancer is the third most common type worldwide in 2020, almost two million cases were diagnosed. As a result, providing new, highly accurate techniques detecting colon leads to early and successful treatment of this disease. This paper aims propose heterogenic stacking deep learning model predict cancer. Stacking integrated with pretrained convolutional neural network (CNN) models metalearner enhance prediction performance. The proposed compared VGG16, InceptionV3, Resnet50, DenseNet121 using different evaluation metrics. Furthermore, are evaluated LC25000 WCE binary muticlassified image datasets. results show that recorded highest performance for For dataset, stacked accuracy, recall, precision, F1 score (100). (98). Stacking-SVM achieved performed existing (VGG16, DenseNet121) because it combines output multiple single trains evaluates produce better predictive than any model. Black-box represented explainable AI (XAI).

Language: Английский

Citations

15

Deep Learning and Federated Learning for Screening COVID-19: A Review DOI Creative Commons
M. Rubaiyat Hossain Mondal, Subrato Bharati, Prajoy Podder

et al.

BioMedInformatics, Journal Year: 2023, Volume and Issue: 3(3), P. 691 - 713

Published: Sept. 1, 2023

Since December 2019, a novel coronavirus disease (COVID-19) has infected millions of individuals. This paper conducts thorough study the use deep learning (DL) and federated (FL) approaches to COVID-19 screening. To begin, an evaluation research articles published between 1 January 2020 28 June 2023 is presented, considering preferred reporting items systematic reviews meta-analysis (PRISMA) guidelines. The review compares various datasets on medical imaging, including X-ray, computed tomography (CT) scans, ultrasound images, in terms number samples, classes datasets. Following that, description existing DL algorithms applied offered. Additionally, summary recent work FL for screening provided. Efforts improve quality models are comprehensively reviewed objectively evaluated.

Language: Английский

Citations

12

Towards classification and comprehensive analysis of AI-based COVID-19 diagnostic techniques: A survey DOI

Amna Kosar,

Muhammad Asif, Maaz Bin Ahmad

et al.

Artificial Intelligence in Medicine, Journal Year: 2024, Volume and Issue: 151, P. 102858 - 102858

Published: April 1, 2024

Language: Английский

Citations

4

Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach DOI

Md. Harun-Or-Roshid,

Kazuhiro Maeda,

Le Thi Phan

et al.

Computers in Biology and Medicine, Journal Year: 2023, Volume and Issue: 169, P. 107848 - 107848

Published: Dec. 13, 2023

Language: Английский

Citations

10

DepneumoNet: A Novel Model for Improved Pneumonia Diagnosis Through Chest X-Ray Imaging DOI
M. Vijayalakshmi,

N. Keerthika,

A. Sasithradevi

et al.

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 305 - 317

Published: Jan. 1, 2025

Language: Английский

Citations

0

A Transfer Learning Strategy to Identify Covid-19 from X-ray DOI

Narenthirakumar Appavu,

Seifedine Kadry, C. Nelson Kennedy Babu

et al.

IETE Journal of Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 13

Published: May 21, 2025

Language: Английский

Citations

0

Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review DOI Creative Commons
Md Shofiqul Islam, Fahmid Al Farid, F. M. Javed Mehedi Shamrat

et al.

PeerJ Computer Science, Journal Year: 2024, Volume and Issue: 10, P. e2517 - e2517

Published: Dec. 24, 2024

The global spread of SARS-CoV-2 has prompted a crucial need for accurate medical diagnosis, particularly in the respiratory system. Current diagnostic methods heavily rely on imaging techniques like CT scans and X-rays, but identifying these images proves to be challenging time-consuming. In this context, artificial intelligence (AI) models, specifically deep learning (DL) networks, emerge as promising solution image analysis. This article provides meticulous comprehensive review imaging-based diagnosis using up May 2024. starts with an overview covering basic steps learning-based data sources, pre-processing methods, taxonomy techniques, findings, research gaps performance evaluation. We also focus addressing current privacy issues, limitations, challenges realm diagnosis. According taxonomy, each model is discussed, encompassing its core functionality critical assessment suitability detection. A comparative analysis included by summarizing all relevant studies provide overall visualization. Considering best deep-learning detection, conducts experiment twelve contemporary techniques. experimental result shows that MobileNetV3 outperforms other models accuracy 98.11%. Finally, elaborates explores potential future directions methodological recommendations advancement.

Language: Английский

Citations

1

Federated Learning Using the Particle Swarm Optimization Model for the Early Detection of COVID-19 DOI
Dasaradharami Reddy Kandati, Gautam Srivastava, Yaodong Zhu

et al.

Communications in computer and information science, Journal Year: 2023, Volume and Issue: unknown, P. 425 - 436

Published: Nov. 25, 2023

Language: Английский

Citations

2

Designing an improved deep learning-based model for COVID-19 recognition in chest X-ray images: a knowledge distillation approach DOI

AmirReza BabaAhmadi,

Sahar Khalafi,

Masoud ShariatPanahi

et al.

Iran Journal of Computer Science, Journal Year: 2023, Volume and Issue: 7(2), P. 177 - 187

Published: Dec. 18, 2023

Language: Английский

Citations

1

Covid-19 A Comprehensive Review of Signs, Symptoms, Diagnosis, and Treatment Strategies DOI Open Access

Mr. Kachare Vishal,

Prof. Waghmare S. U.,

Poonam B. Kodage

et al.

International Journal of Advanced Research in Science Communication and Technology, Journal Year: 2024, Volume and Issue: unknown, P. 51 - 68

Published: May 8, 2024

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome 2 (SARS-CoV-2) has significantly impacted global health. This review aims to provide a comprehensive overview of the signs, symptoms, diagnosis, and treatment modalities COVID-19. clinical presentation COVID-19 varies widely, ranging from asymptomatic or mild symptoms distress multiorgan failure. Common include fever, cough, fatigue, dyspnea, with less frequent such as anosmia, ageusia, gastrointestinal symptoms. Diagnosis primarily relies on reverse transcription-polymerase chain reaction (RT-PCR) testing specimens. However, imaging chest X-ray Antibody Test Antigen test in especially cases atypical presentations. Treatment strategies supportive care, antiviral therapy, and, cases, other intensive care measures. development distribution vaccines have been pivotal controlling spread virus. Despite significant progress understanding managing COVID-19, ongoing research is crucial refine diagnostic strategies, develop effective therapies, improve patient outcomes. Antiviral drugs, remdesivir, poxolovid, molonupiravir, widely used inhibit viral replication reduce severity duration Immunomodulators, including tocilizumab target specific pathways involved hyperinflammatory response seen Monoclonal antibodies, casirivimab/imdevimab sotrovimab, employed for passive immunization neutralize virus risk progression

Language: Английский

Citations

0