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Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

Medicine and Health

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

X. Xue, S. Chinnaperumal, et al.

Discover a groundbreaking deep learning ensemble framework for detecting COVID-19 and pneumonia through CT scan and X-ray images. This innovative model, developed by Xingsi Xue and colleagues, boasts an impressive 99% accuracy and outperforms existing methods. Don't miss the chance to explore how advanced transfer learning techniques can revolutionize medical imaging diagnostics.

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Playback language: English
Abstract
This research proposes a deep learning ensemble framework for detecting COVID-19 and pneumonia using large-scale CT scan and X-ray image datasets. The framework utilizes ResNet152, VGG16, ResNet50, and DenseNet121, employing transfer learning to improve efficiency. An enhanced VGG16 model achieves 99% accuracy in multi-class classification of X-ray images. The proposed model demonstrates superior performance compared to existing methods, achieving an average F-score of 95–97%.
Publisher
Bioengineering
Published On
Mar 16, 2023
Authors
Xingsi Xue, Seelammal Chinnaperumal, Ghaida Muttashar Abdulsahib, Rajasekhar Reddy Manyam, Raja Marappan, Sekar Kidambi Raju, Osamah Ibrahim Khalaf
Tags
COVID-19 detection
pneumonia detection
deep learning
transfer learning
CT scan
X-ray images
ensemble framework

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