U-Net Based Covid-19 Infected Lesion Detection and Deep Learning Based Classification on CXR - Balik, Esra; Kaya, Buket; Kaya, Mehmet (Institute of Electrical and Electronics Engineers Inc., 2022)
The Covid-19 disease, which emerged in China in December 2019 and caused by the coronavirus virus, soon became a pandemic all over the world. The fact that the Transcription Polymerase Chain Reaction (RT-PCR) test produces false negatives in some studies and the diagnosis time is long, has led to the search for new alternatives for the diagnosis of this virus, which can result in death, especially with the damage it causes to the lungs. Therefore, chest images have become suitable tools for diagnosis from chest images with data obtained from Computed Tomography or CXR imaging techniques. Deep learning studies have been proposed to provide diagnosis with these tools and to determine the infected region of Covid-19 and Pneumonia disease. In this paper, a two-stage system is proposed as segmentation and classification. In the segmentation process, infected regions segmented from the labeled data were determined. In the classifier stage, Covid- 19/Pneumonia/Normal classification was performed using three different deep learning models named VGG16, ResNet50 and InceptionV3. To the best of our knowledge, this is the first attempt to sequentially design classification and segmentation systems into a more precise diagnosis. As a result of the study, 95% segmentation accuracy was obtained. Classifier models achieved 99%, 90% and 98% accuracy, respectively. © 2022 IEEE.
2022 International Conference on Data Analytics for Business and Industry, ICDABI 2022 -- 25 October 2022 through 26 October 2022 -- Virtual, Online -- 186761
Keywords: classification; CNN; COVID-19; CXR; InceptionV3; ResNet50; segmentation.; U-Net; VGG16