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

Xingsi Xue; Seelammal Chinnaperumal; Ghaida Muttashar Abdulsahib; Rajasekhar Reddy Manyam; Raja Marappan; Sekar Kidambi Raju; Osamah Ibrahim Khalaf

doi:10.3390/bioengineering10030363

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

Bioengineering (Basel). 2023 Mar 16;10(3):363. doi: 10.3390/bioengineering10030363.

Authors

Xingsi Xue¹, Seelammal Chinnaperumal², Ghaida Muttashar Abdulsahib³, Rajasekhar Reddy Manyam⁴, Raja Marappan⁵, Sekar Kidambi Raju⁵, Osamah Ibrahim Khalaf⁶

Affiliations

¹ Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350011, China.
² Department of Computer Science and Engineering, Solamalai College of Engineering, Madurai 625020, Tamil Nadu, India.
³ Department of Computer Engineering, University of Technology, Baghdad 10066, Iraq.
⁴ Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati Campus, Mangalagiri 522503, Andhra Pradesh, India.
⁵ School of Computing, SASTRA Deemed University, Thanjavur 613401, Tamil Nadu, India.
⁶ Department of Solar, Al-Nahrain Renewable Energy Research Center, Al-Nahrain University, Baghdad 64040, Iraq.

Abstract

Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures such as chest X-rays and computed tomography (CT) scans. Deep learning (DL) and artificial intelligence (AI) are critical tools for early and accurate detection of COVID-19. This research explores the different DL techniques for identifying COVID-19 and pneumonia on medical CT and radiography images using ResNet152, VGG16, ResNet50, and DenseNet121. The ResNet framework uses CT scan images with accuracy and precision. This research automates optimum model architecture and training parameters. Transfer learning approaches are also employed to solve content gaps and shorten training duration. An upgraded VGG16 deep transfer learning architecture is applied to perform multi-class classification for X-ray imaging tasks. Enhanced VGG16 has been proven to recognize three types of radiographic images with 99% accuracy, typical for COVID-19 and pneumonia. The validity and performance metrics of the proposed model were validated using publicly available X-ray and CT scan data sets. The suggested model outperforms competing approaches in diagnosing COVID-19 and pneumonia. The primary outcomes of this research result in an average F-score (95%, 97%). In the event of healthy viral infections, this research is more efficient than existing methodologies for coronavirus detection. The created model is appropriate for recognition and classification pre-training. The suggested model outperforms traditional strategies for multi-class categorization of various illnesses.

Keywords: COVID-19 detection; CT scan images; chest X-ray; deep learning; transfer learning.

Grants and funding

2020J01875/This work is supported by the National Natural Science Foundation of China (No. 62172095), the Natural Science Foundation of Fujian Province (Nos. 2020J01875 and 2022J01644).