Abstract
COVID-19 is a deadly and highly infectious pneumonia type disease. RT-PCR is a proven testing methodology for the detection of coronavirus infection in spite of having a lengthy testing time. Sometimes, it gives false-positive results more than the desired rates. To support the conventional RT-PCR methodology or testing independently without RC-PCR methodology for correct clinical diagnosis, COVID-19 testing can be acquired with images of X-Ray and CT Scan of a person. This image-based analysis will make a radical change in the detection of coronavirus in the human body with negligible false-negative and false-positive results. For the detection of COVID-19 in CT Scan and X-Ray images of coronavirus suspected individuals, this paper uses a multi-image augmented Convolutional Neural Network (CNN). For training the CNN model, multi-image augmentation utilizes discontinuity information acquired from the edged images to increase the meaningful examples. With this method, the proposed model exhibits a higher classification accuracy of around 98.97% for X-Ray and 95.38% for CT Scan images. Using multi-image augmentation, X-Ray images achieve a specificity of 98.88% and a sensitivity of 99.07% whereas a specificity of 95.98% and sensitivity of 94.78% are achieved in CT Scan images. The experimental results are also compared with VGG-16 and ResNet-50 models. The evaluation has been performed on publicly available databases comprising chest images of both X-Ray and CT Scan.
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Purohit, K., Kesarwani, A., Ranjan Kisku, D., Dalui, M. (2022). COVID-19 Detection on Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model. In: Giri, D., Raymond Choo, KK., Ponnusamy, S., Meng, W., Akleylek, S., Prasad Maity, S. (eds) Proceedings of the Seventh International Conference on Mathematics and Computing . Advances in Intelligent Systems and Computing, vol 1412. Springer, Singapore. https://doi.org/10.1007/978-981-16-6890-6_30
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