TY - JOUR
T1 - Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection
T2 - A Comprehensive Review
AU - Ilesanmi, Ademola E.
AU - Ilesanmi, Taiwo
AU - Ajayi, Babatunde
AU - Gbotoso, Gbenga A.
AU - Belhaouari, Samir Brahim
N1 - Publisher Copyright:
© The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine 2025.
PY - 2025/10
Y1 - 2025/10
N2 - The advent of three-dimensional convolutional neural networks (3D CNNs) has revolutionized the detection and analysis of COVID-19 cases. As imaging technologies have advanced, 3D CNNs have emerged as a powerful tool for segmenting and classifying COVID-19 in medical images. These networks have demonstrated both high accuracy and rapid detection capabilities, making them crucial for effective COVID-19 diagnostics. This study offers a thorough review of various 3D CNN algorithms, evaluating their efficacy in segmenting and classifying COVID-19 across a range of medical imaging modalities. This review systematically examines recent advancements in 3D CNN methodologies. The process involved a comprehensive screening of abstracts and titles to ensure relevance, followed by a meticulous selection and analysis of research papers from academic repositories. The study evaluates these papers based on specific criteria and provides detailed insights into the network architectures and algorithms used for COVID-19 detection. The review reveals significant trends in the use of 3D CNNs for COVID-19 segmentation and classification. It highlights key findings, including the diverse range of networks employed for COVID-19 detection compared to other diseases, which predominantly utilize encoder/decoder frameworks. The study provides an in-depth analysis of these methods, discussing their strengths, limitations, and potential areas for future research. The study reviewed a total of 60 papers published across various repositories, including Springer and Elsevier. The insights from this study have implications for clinical diagnosis and treatment strategies. Despite some limitations, the accuracy and efficiency of 3D CNN algorithms underscore their potential for advancing medical image segmentation and classification. The findings suggest that 3D CNNs could significantly enhance the detection and management of COVID-19, contributing to improved healthcare outcomes.
AB - The advent of three-dimensional convolutional neural networks (3D CNNs) has revolutionized the detection and analysis of COVID-19 cases. As imaging technologies have advanced, 3D CNNs have emerged as a powerful tool for segmenting and classifying COVID-19 in medical images. These networks have demonstrated both high accuracy and rapid detection capabilities, making them crucial for effective COVID-19 diagnostics. This study offers a thorough review of various 3D CNN algorithms, evaluating their efficacy in segmenting and classifying COVID-19 across a range of medical imaging modalities. This review systematically examines recent advancements in 3D CNN methodologies. The process involved a comprehensive screening of abstracts and titles to ensure relevance, followed by a meticulous selection and analysis of research papers from academic repositories. The study evaluates these papers based on specific criteria and provides detailed insights into the network architectures and algorithms used for COVID-19 detection. The review reveals significant trends in the use of 3D CNNs for COVID-19 segmentation and classification. It highlights key findings, including the diverse range of networks employed for COVID-19 detection compared to other diseases, which predominantly utilize encoder/decoder frameworks. The study provides an in-depth analysis of these methods, discussing their strengths, limitations, and potential areas for future research. The study reviewed a total of 60 papers published across various repositories, including Springer and Elsevier. The insights from this study have implications for clinical diagnosis and treatment strategies. Despite some limitations, the accuracy and efficiency of 3D CNN algorithms underscore their potential for advancing medical image segmentation and classification. The findings suggest that 3D CNNs could significantly enhance the detection and management of COVID-19, contributing to improved healthcare outcomes.
KW - 3D Convolutional neural network
KW - Computed Tomography (CT)
KW - Covid-19
KW - Medical images
KW - Segmentation and classification
UR - https://www.scopus.com/pages/publications/105007828997
U2 - 10.1007/s10278-025-01393-x
DO - 10.1007/s10278-025-01393-x
M3 - Review article
AN - SCOPUS:105007828997
SN - 2948-2933
VL - 38
SP - 2915
EP - 2933
JO - Journal of Imaging Informatics in Medicine
JF - Journal of Imaging Informatics in Medicine
IS - 5
ER -