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|Title:||Different Deep Learning Based Classification Models for COVID-19 CT-Scans and Lesion Segmentation Through the cGAN-UNet Hybrid Method||Authors:||Ngong, Ivoline C.
Baykan, Nurdan Akhan
|Keywords:||COVID-19 segmentation COVID-19
classification conditional generative
adversarial network (cGAN) convolutional
neural network (CNN) PatchCNN capsule
neural network (CapsNet)
|Issue Date:||2023||Publisher:||Int Information & Engineering Technology Assoc||Abstract:||The new coronavirus, which emerged in early 2020, caused a major global health crisis in 7 continents. An essential step towards fighting this virus is computed tomography (CT) scans. CT scans are an effective radiological method to detecting the diagnosis in early stage, but have greatly increased the workload of radiologists. For this reason, there are systems needed that will reduce the duration of CT examinations and assist radiologists. In this study, a two-stage system has been proposed for COVID-19 detection. First, a hybrid method is proposed that can segment the infected region from CT images. The reason for this is that there is not always a reference image in the datasets used in the classification. For this purpose; UNet, UNet++, SegNet and PsPNet were used both separately and as hybrids with GAN, to automatically segment infected areas from chest CT slices. According to the segmentation results, cGAN-UNet hybrid system was selected as the most successful method. Experimental results show that the proposed method achieves a segmentation success with a dice score of 92.32% and IoU score of 86.41%. In the second stage, three classifiers which include a Convolutional Neural Network (CNN), a PatchCNN and a Capsule Neural Network (CapsNet) were used to classify the generated masks as either COVID-19 or not, using the segmented images obtained from cGAN-UNet. Success of these classifiers was 99.20%, 92.55% and 73.84%, respectively. According to these results, the highest success was achieved in the system where cGAN-Unet and CNN are used together.||URI:||https://doi.org/10.18280/ts.400101
|Appears in Collections:||Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections|
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections
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checked on Sep 25, 2023
checked on Sep 25, 2023
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