Covid-19 Detection from Chest X-Ray Images using Convolutional Neural Network

• Published in: 2021 2nd International Conference on Artificial Intelligence and Data Sciences, AiDAS 2021
• Main Author: Keram U.B.; Ramli M.A.B.M.; Kamal N.A.M.; Abas L.H.B.M.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2021
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118983640&doi=10.1109%2fAiDAS53897.2021.9574345&partnerID=40&md5=bb2ffe7fc8bebc9bc33e437655d92e92

COVID-19 reported cases in Malaysia is increasing every day. The lab facilities for testing COVID-19 has almost reached their capacity. Traditionally, to detect the COVID-19, a swab test is used. This method will take some time and be costly. The swab test kits are very scarce, and the human resources to do this test are limited. Through modern technology, there is a probability of detecting positive COVID-19 using X-ray images with deep learning. In this paper, Convolutional Neural Network (CNN) approach is used to detect Covid-19 through digital X-ray images. The 2D convolution kernel layer consists of three layers. The first layer has a 3 × 3 kernel, the second part has a 5 × 5 kernel, and the third part has a 7 × 7 kernel. Then, the output will be combined into one layer. Afterwards, the concatenated layer continued with another sequential process consisting of two convolution processes, ReLU and max pooling. Next, the model is then flattened, dropout and dense. A total of 2100 positive Covid-19 and negative Covid-19 images from Github and Kaggle databases have been used in this research. Based on the experiment done, the accuracy was almost 96%. © 2021 IEEE.