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| dc.contributor.author | Zewdie, Habtie Sisay | |
| dc.date.accessioned | 2022-11-21T07:34:05Z | |
| dc.date.available | 2022-11-21T07:34:05Z | |
| dc.date.issued | 2022-08-22 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/14498 | |
| dc.description.abstract | The appearance of the Coronavirus disease 2019 (COVID-19) caused a global crisis both socially and economically. Reverse Transcription Polymerase Chain Reaction (RT-PCR) is currently being used as a test kit for detecting the presence of the virus in the patient. However, RT-PCRs are limited in number and costly to achieve the required number of tests and they produce many false-negative results both in the initial tests and in recovered patients. As a result, many deep learning (DL) algorithms are developed and achieved state-of-the-art performance in detecting the virus and identifying the severity stage. However, these methods used old Convolutional Neural Network (CNN) models that are computationally expensive and complex. In this research work, we designed a two-stage DL model using an approach known as Vision Transformer (ViT) that can detect COVID-19 and determine its severity stage using thoracic CT images. In the first stage of the DL model, we used a pre-trained ViT model called ViT_B/32 to classify CT images into COVID and non-COVID. We also designed our own simple custom CNN model and performed extensive set of experiments for the detection of COVID-19 using both ViT_B/32 and CNN models in the first stage network. In the second stage of the DL model, we used a U-Net like ViT based model known as Vision Transformer for Biomedical Image Segmentation (VITBIS) to segment both the lung and infection regions of the COVID infected CT images and computed the severity level of the infection. Transformers with attention mechanism are used both in the encoder and decoder parts of the VITBIS instead of CNN encoder and decoder architecture. The second stage DL network contains two sub-models; one for the lung segmentation and the other for the lesion segmentation. In the first stage network, the ViT model outperformed the CNN models and achieved a 5-fold cross validated accuracy result of 99.7%. Our custom CNN model is a runner up with a 5-fold cross validated accuracy result of 98%. The VGG16 pre-trained CNN model took the third place with accuracy result of 97%. For the second stage network, the best performance of the lung segmentation network is 95.8% Intersection Over Union (IOU), 96.22% Dice similarity Coefficient (DSC) and sensitivity of 99.69%. The lesion segmentation network performed with IOU of 94%, DSC of 95.23% and sensitivity of 98.3%. Keywords: COVID-19, Deep Learning, Severity, Thoracic CT | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Faculty of Electrical and Computer Engineering | en_US |
| dc.title | A deep learning approach for identification and severity classification of covid-19 cases using thoracic ct images | en_US |
| dc.type | Thesis | en_US |
