An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

Applied Sciences

Jehad Ali¹  Muhammad Idrees²  Mehedi Masud³  Attique Ur Rehman⁴  Syed Hasnain Raza Kazmi⁵  Muhammad Shoaib Farooq⁵  Muhammad Ahsan Raza⁶  Jehad F. Al-Amri⁷ 

[1] Department of Computer Engineering, and Department of AI Convergence Network, Ajou University, Suwon 16499, Korea;Department of Computer Science and Engineering, UET Lahore, Narowal Campus, Lahore 54890, Pakistan;Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;Department of Computer Science, Lahore Garrison University, Lahore 54000, Pakistan;Department of Computer Science, School of System and Technology, University of Management and Technology, Lahore 54000, Pakistan;Department of Information Technology, Bahauddin Zakariya University, Multan 60000, Pakistan;Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;

关键词: convolutional;    COVID-19;    neural network;    chest X-ray;    model;    detection;   

DOI  :  10.3390/app112110301

来源: DOAJ

【 摘 要 】

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

【 授权许可】

Unknown