Abstract
Healthcare systems worldwide have been struggling since the beginning of the COVID-19 pandemic. The early diagnosis of this unprecedented infection has become their ultimate objective. Detecting positive patients from chest X-ray images is a quick and efficient solution for overloaded hospitals. Many studies based on deep learning (DL) techniques have shown high performance in classifying COVID-19 chest X-ray images. However, most of these studies suffer from a class imbalance problem mainly due to the limited number of COVID-19 samples. Such a problem may significantly reduce the efficiency of DL classifiers. In this work, we aim to build an accurate model that assists clinicians in the early diagnosis of COVID-19 using balanced data. To this end, we trained six state-of-the-art convolutional neural networks (CNNs) via transfer learning (TL) on three different COVID-19 datasets. The models were developed to perform a multi-classification task that distinguishes between COVID-19, normal, and viral pneumonia cases. To address the class imbalance issue, we first investigated the Weighted Categorical Loss (WCL) and then the Synthetic Minority Oversampling Technique (SMOTE) on each dataset separately. After a comparative study of the obtained results, we selected the model that achieved high classification results in terms of accuracy, sensitivity, specificity, precision, F1 score, and AUC compared to other recent works. DenseNet201 and VGG-19 claimed the best scores. With an accuracy of 98.87%, an F1_Score of 98.21%, a sensitivity of 98.86%, a specificity of 99.43%, a precision of 100%, and an AUC of 99.15%, the WCL combined with CheXNet outperformed the other examined models.
【저자키워드】 deep learning, classification, COVID-19 diagnosis, transfer learning, Chest X-ray images, Data imbalance, 【초록키워드】 COVID-19, COVID-19 pandemic, Infection, hospitals, Viral pneumonia, early diagnosis, sensitivity, specificity, Accuracy, dataset, synthetic, COVID-19 samples, Efficiency, AUC, Chest X-ray image, Precision, clinician, technique, transfer, datasets, CNNs, classifiers, Loss, selected, shown, examined, significantly, investigated, reduce, positive patient, assist, claimed, build, multi-classification, 【제목키워드】 COVID-19, Chest X-ray image,