Open AccessUtilization of Transfer Learning Model in Detecting COVID-19 cases from Chest X-Ray Images
Publication year - 2022
Publication title -
international journal of e-health and medical communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.151
H-Index - 12
eISSN - 1947-3168
pISSN - 1947-315X
DOI - 10.4018/ijehmc.20220701oa02
Subject(s) - transfer of learning , covid-19 , support vector machine , artificial intelligence , computer science , robustness (evolution) , feature extraction , classifier (uml) , pattern recognition (psychology) , deep learning , machine learning , medicine , biochemistry , chemistry , disease , pathology , infectious disease (medical specialty) , gene
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%.