Advancements in Automated Detection of COVID-19 in Human Chest CT Scans Using DLNN Techniques

被引：0

作者：

Javhav A. ^{[1
]}

Pujari S. ^{[2
]}

机构：

[1] Department of Civil ECE, Angadi Institute of Technology and Management, Karnataka, Belagavi

[2] Department of ECE, KLE College of Engineering & Technology, Chikodi, Karnataka, Belagavi

来源：

Trends in Biomaterials and Artificial Organs | 2024年 / 38卷 / 02期

关键词：

Covid-19; CT scan; DLNN; performance evaluation;

D O I：

10.5281/zenodo.12186903

中图分类号：

学科分类号：

摘要：

The automated recognition of COVID-19 in human Chest CT scans has emerged as a vital tool in the fight against the global pandemic. This paper presents an outline of recent advancements in the automated recognition of COVID-19 in human chest CT scans through the application of deep learning neural network techniques. Chest CT scans have been proven to be a valuable tool for identifying COVID-19-related abnormalities in the lungs, and the integration of DL models have significantly enhanced the efficiency and accuracy of this process.This paper also explores the evolving landscape of automated COVID-19 identification, highlighting the role of deep learning in transforming diagnostic capabilities. It discusses the challenges, including data quality and privacy concerns, as well as the promising solutions that have emerged. The data gram is enhanced by using DLNN, MobileNetV2, DenseNet and GoogLeNet. The compilation time required for DLNN is more compared with other technique it takes nearly 1h, 53min and results 97.3% accuracy in detection of COVID-19. © (2024) Society for Biomaterials & Artificial Organs.

引用

页码：99 / 104

页数：5

共 35 条

[21]

Cohen J. P., Morrison P., Dao L., COVID-19 Image Data Collection, (2020)

[22]

Zhang J. Xie, Li Y., Shen C., Xia Y., COVID-19 Screening On Chest X-Ray Images Using Deep Learning Based Anomaly Detection, (2020)

[23]

Zhao J., Zhang Y., He X., Xie P., COVID-CT-Dataset: A CT Scan Dataset About COVID-19

[24]

Li L., Et al., Artificial Intelligence Distinguishes COVID-19 From Community Acquired Pneumonia On Chest CT, Radiology, (2020)

[25]

Tang L., Zhang X., Wang Y., Zeng X., Severe COVID-19 Pneumonia: Assessing Inflammation Burden With Volume-Rendered Chest CT, Radiology, Cardiothoracic Imag, 2, 2, (2020)

[26]

Bai H.X., Et al., Performance of Radiologists in Differentiating COVID-19 From Viral Pneumonia On Chest CT, Radiology, 296, 2, (2020)

[27]

Wong H.Y.F., Et al., Frequency and Distribution of Chest Radiographic Findings in COVID-19 Positive Patients, Radiology, 296, 2, (2020)

[28]

Ren K.S., Girshick R., Sun J., Faster R-Cnn R., Towards Real-Time Object Detection with Region Proposal Networks, Advances in Neural Information Processing Systems, pp. 91-99, (2015)

[29]

Ardila D., Et al., End-To-End Lung Cancer Screening with Three-Dimensional Deep Learning On Low-Dose Chest Computed Tomography, Nature Medicine, 25, pp. 954-961, (2019)

[30]

Zhang K., Et al., Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements and Prognosis of COVID-19 Pneumonia Using Computed Tomography, Cell, 181, pp. 1423-1433, (2020)

← 1 2 3 4 →