Automated detection and forecasting of covid-19 using deep learning techniques: A review

A Shoeibi, M Khodatars, M Jafari, N Ghassemi… - Neurocomputing, 2024 - Elsevier
Abstract In March 2020, the World Health Organization (WHO) declared COVID-19 a global
epidemic, caused by the SARS-CoV-2 virus. Initially, COVID-19 was diagnosed using real …

[HTML][HTML] Deep learning for pneumonia detection in chest x-ray images: A comprehensive survey

R Siddiqi, S Javaid - Journal of imaging, 2024 - mdpi.com
This paper addresses the significant problem of identifying the relevant background and
contextual literature related to deep learning (DL) as an evolving technology in order to …

Enhancing computer-aided cervical cancer detection using a novel fuzzy rank-based fusion

P Sahoo, S Saha, S Mondal, M Seera… - IEEE …, 2023 - ieeexplore.ieee.org
Cervical cancer is a severe and pervasive disease that poses a significant health threat to
women globally. The Pap smear test is an efficient and effective method for detecting …

A multistage framework for respiratory disease detection and assessing severity in chest X-ray images

P Sahoo, SK Sharma, S Saha, D Jain, S Mondal - Scientific reports, 2024 - nature.com
Chest Radiography is a non-invasive imaging modality for diagnosing and managing
chronic lung disorders, encompassing conditions such as pneumonia, tuberculosis, and …

COVID-19 detection from Chest X-ray images using a novel lightweight hybrid CNN architecture

P Pradeep Dalvi, D Reddy Edla… - Multimedia Tools and …, 2024 - Springer
The pandemic of COVID-19 has affected worldwide population. Diagnosing this highly
contagious disease at an initial stage is essential for controlling its spread. In this paper, we …

Fedmrl: Data heterogeneity aware federated multi-agent deep reinforcement learning for medical imaging

P Sahoo, A Tripathi, S Saha, S Mondal - International Conference on …, 2024 - Springer
Despite recent advancements in Federated Learning (FL) for medical image diagnosis,
addressing data heterogeneity among clients remains a significant challenge for practical …

Chaotic satin bowerbird optimizer based advanced AI techniques for detection of COVID-19 diseases from CT scans images

V Uma Maheswari, S Stephe, R Aluvalu… - New Generation …, 2024 - Springer
Abstract Background The SARS-CoV-2 virus, which caused the COVID-19 pandemic,
emerged in late 2019, leading to significant global health challenges due to the lack of …

Interpretable COVID-19 chest X-ray detection based on handcrafted feature analysis and sequential neural network

R Prince, Z Niu, ZY Khan, J Chambua, A Yousif… - Computers in Biology …, 2025 - Elsevier
Deep learning methods have significantly improved medical image analysis, particularly in
detecting COVID-19 chest X-rays. Nonetheless, these methodologies frequently inhibit some …

A high-accuracy lightweight network model for X-ray image diagnosis: A case study of COVID detection

S Wang, J Ren, X Guo - Plos one, 2024 - journals.plos.org
The Coronavirus Disease 2019 (COVID-19) has caused widespread and significant harm
globally. In order to address the urgent demand for a rapid and reliable diagnostic approach …

Detection of Severe Lung Infection on Chest Radiographs of COVID-19 Patients: Robustness of AI Models across Multi-Institutional Data

A Sobiecki, LM Hadjiiski, HP Chan, RK Samala… - Diagnostics, 2024 - mdpi.com
The diagnosis of severe COVID-19 lung infection is important because it carries a higher risk
for the patient and requires prompt treatment with oxygen therapy and hospitalization while …