Browsing by Author "SP20-RCS-019"

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    Deep Learning-Based Prediction of SARS-CoV 2 (COVID-19) and its Severity Classification using Multimodal Chest Radiography Images
    (Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Mughees Ahmad; SP20-RCS-019; LHR TP 7595; Dr. Usama Ijaz Bajwa
    The new COVID-19 emerged in a town in China named Wuhan in December 2019 and since then this deadly virus transmitted all over the world. This transmittable disease has infected 262 million people worldwide and 5.2 million deaths till November 2021. As we can see the rapidly spreading of this pandemic, different countries are facing limited resources such as medical test kits and ventilators because of the number of positive cases that have been increased out of control. In this devastating situation, it is very compulsory to develop an easily available, low-priced, and automatic deep learning model for COVID-19 prediction through chest radiography images (CRIs) such as X-rays, and CTs. The proposed study is using chest radiography images to detect chest infections such as bacterial, viral, and COVID-19 infection as X-rays and CTs give significant information about this deadly infection. The recent hybrid deep learning (DL) techniques can be used along with chest radiography images (CRIs) for the faster and accurate detection of different chest infections including COVID-19 and its severity levels (i.e., negative for pneumonia, atypical appearance, indeterminate appearance, and typical appearance). Therefore, a novel hybrid model named Lightweight Residual_Bi-GRU uses residual blocks and Bi-directional gated recurrent unit (Bi-GRU) for automatic and correct detection of non-COVID and COVID-19 infections by using preprocessed chest radiography images (CRIs). Lightweight Residual_Bi-GRU is used for the recognition of two-class classification (normal and COVID-19), three-class classification (normal vs COVID-19 vs viral pneumonia), four class classification (normal vs COVID-19 vs viral pneumonia vs bacterial pneumonia), and different COVID-19 severity types' classification. The presented model provides a classification accuracy of 99.5%, 98.4%, 90.2%, and 80.7% for 2 class, 3 class, 4 class, and COVID-19 severity levels classifications. The results prove that radiologists and medical officers can adopt this method for the screening of chest infections where test kits are limited.