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Wang, X., Peng, Y., Lu, L.Z. and Summers, R.M. (2018) Tienet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, 18-23 June 2018, 9049-9058.
https://doi.org/10.1109/CVPR.2018.00943

has been cited by the following article:

• TITLE: COVID-19 Detection from Chest X-Ray Images Using Convolutional Neural Network Approach

  AUTHORS: Md. Harun Or Rashid, Muzakkir Hossain Minhaz, Ananya Sarker, Must. Asma Yasmin, Md. Golam An Nihal

  KEYWORDS: COVID-19, Chest X-Ray Images, CNN, Virus, Accuracy

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.5, May 26, 2023

  ABSTRACT: COVID-19 is a respiratory illness caused by the SARS-CoV-2 virus, first identified in 2019. The primary mode of transmission is through respiratory droplets when an infected person coughs or sneezes. Symptoms can range from mild to severe, and timely diagnosis is crucial for effective treatment. Chest X-Ray imaging is one diagnostic tool used for COVID-19, and a Convolutional Neural Network (CNN) is a popular technique for image classification. In this study, we proposed a CNN-based approach for detecting COVID-19 in chest X-Ray images. The model was trained on a dataset containing both COVID-19 positive and negative cases and evaluated on a separate test dataset to measure its accuracy. Our results indicated that the CNN approach could accurately detect COVID-19 in chest X-Ray images, with an overall accuracy of 97%. This approach could potentially serve as an early diagnostic tool to reduce the spread of the virus.