CNN supported automated recognition of Covid-19 infection in chest X-ray images. (2022)

• Record Type: Journal Article
• Authors: Padmakala, S.; Revathy, S.; Vijayalakshmi, K.; Mathankumar, M.
• Is Part Of: Materials today. Volume 66:Part 3(2022)
• Page Start: 1201
• Page End: 1210
• Publication Date: 2022
• Subjects: Chest X-ray -- Resnet18 -- PyTorch -- Canny algorithm; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2022.05.003
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: Automatic recognition of lung system is use to identify normal and covid infected lungs from chest X-ray images of the people. In the year 2020, the coronavirus forcefully pushed the entire world into a freakish situation, the foremost challenge is to diagnosis the coronavirus. We have got standard diagnosis test called PCR test which is complex and costlier to check the patient's sample at initial stage. Keeping this in mind, we developed a work to recognize the chest X-ray image automatically and label it as Covid or normal lungs. For this work, we collected the dataset from open-source data repository and then pre-process each X-ray images from each category such as covid X-ray images and non-covid X-ray images using various techniques such as filtering, edge detection, segmentation, etc., and then the pre-processed X-ray images are trained using CNN-Resnet18 network. Using PyTorch python package, the resnet-18 network layer is created which gives more accuracy than any other algorithm. From the acquired knowledge the model is correctly classifies the testing X-ray images. Then the performance of the model is calculated and analyzed with various algorithms and hence gives that the resnet-18 network improves our model performance in terms of specificity and sensitivity with more than 90%.