Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images

L. Anbarasi, Malathy Jawahar, Prassanna Jayachandran, Manikandan Ramachandran, Fadi Al-Turjman

OAI: oai:igi-global.com:280364 • DOI: 10.4018/IJEHMC.20220701.oa2

Diagnosis of COVID-19 pneumonia using patients’ chest X-Ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest X-Ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and Support Vector Machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm and it was found to have a high classification accuracy of 95%.