Biomedical Engineering

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Browsing Biomedical Engineering by Subject "Anthropomorphic Data"

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    Artificial Intelligence-Based Clinical Assistance Tool for COVID-19 Cases in Ethiopia
    (Addis Ababa University, 2022-12) Mastewal Mathiwos; Dawit Assefa (PhD); Robel Kebede (PhD) Co-Advisor
    The COVID-19 crisis has had a devastating impact in terms of loss of human life and economic disruption. According to a world health organization report on March 2022, more than 6.1 million people have died worldwide as a result of this pandemic. Early and precise recognition of COVID-19 is key for treating patients and slowing the spread of the pandemic. Several artificial intelligence (AI) based solutions have been developed to facilitate the application of chest X-ray (CXR) imaging and anthropomorphic data for use as a COVID-19 screening tool in resource-limited settings. The current study aims to develop a COVID-19 diagnosis scheme based on a deep learning (DL) approach applied on X-ray image samples collected locally in Ethiopia as well as a severity prediction tool based on a machine learning (ML) approach applied on anthropomorphic data collected from the same patients. The study data for the DL approach consisted of 746 CXR labeled images collected from St. Peter’s Specialized Hospital (SPSH) and Millennium COVID-19 care center (MCCC) in Ethiopia. The samples were composed of images acquired from patients treated for COVID-19 and normal controls. The DL model involved data preprocessing steps applied on the raw CXR images and was designed based on transfer learning approaches. The diagnostic prediction ability of the approach was measured in terms of probability score value and occlusion sensitivity map. The study data for the ML model development consisted of 308 anthropomorphic data (including demographic, comorbidity, and COVID-19 symptoms) collected from the MCCC to check the severity level of the COVID-19 cases and classify them into one of the three classes: moderate, sever or critical. A 5-fold cross-validation approach was used to train two popular ML approaches namely Support Vector Machine (SVM) and K-nearest Neighborhood (KNN) models. The DL method using ResNet-50 architecture achieved best classification performance with a validation accuracy of 94.20 % in accurately classifying COVID-19 and normal cases. The SVM model achieved a better prediction ability than K-NN with an overall accuracy of 89.9 % in predicting the severity status of the COVID-19 cases.