A comparative study of multiple neural network for detection of COVID-19 on chest X-ray

EURASIP Journal on Advances in Signal Processing

Anis Shazia¹  Juliana Usman¹  Khin Wee Lai¹  Tan Zi Xuan¹  Joon Huang Chuah²  Pengjiang Qian³ 

[1] Department of Biomedical Engineering, Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia;Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia;School of Artificial Intelligence and Computer Science, Jiangnan University, 1800 Lihu Avenue, 214122, Wuxi, Jiangsu, People’s Republic of China;

关键词: Artificial neural networks;    Deep learning;    Transfer learning;    Multi-task learning;    COVID-19;    Classification;    DenseNet121;   

DOI  :  10.1186/s13634-021-00755-1

来源: Springer

【 摘 要 】

Coronavirus disease of 2019 or COVID-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers, it is becoming overwhelming for the healthcare workers to rapidly diagnose the condition and contain it from spreading. Hence it has become a necessity to automate the diagnostic procedure. This will improve the work efficiency as well as keep the healthcare workers safe from getting exposed to the virus. Medical image analysis is one of the rising research areas that can tackle this issue with higher accuracy. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, Resnet50, and Xception) to deal with the detection and classification of coronavirus pneumonia from pneumonia cases. This study uses 7165 chest X-ray images of COVID-19 (1536) and pneumonia (5629) patients. Confusion metrics and performance metrics were used to analyze each model. Results show DenseNet121 (99.48% of accuracy) showed better performance when compared with the other models in this study.

【 授权许可】

CC BY