COVID-19 Diagnosis using Single-modality and Joint Fusion Deep Convolutional Neural Network Models

COVID-19 is a recently emerged pneumonia disease with threatening complications that can be avoided by early diagnosis. Deep learning (DL) multimodality fusion is rapidly becoming state of the art, leading to enhanced performance in various medical applications such as cognitive impairment diseases and lung cancer. In this paper, for COVID-19 detection, seven deep learning models (VGG19, DenseNet121, InceptionV3, InceptionResNetV2, Xception, ResNet50V2, and MobileNetV2) using single-modality and joint fusion were empirically examined and contrasted in terms of accuracy, area under the curve, sensitivity, specificity, precision, and Fl-score with Scott-Knott Effect Size Difference statistical test and Borda Count voting method. The empirical evaluations were conducted over two datasets: COVID-19 Radiography Database and COVID-CT using 5-fold cross validation. Results showed that MobileNetV2 was the best performing and less sensitive technique on the two datasets using mono-modality with an accuracy value of 78% for Computed Tomography (CT) and 92% for Chest X-Ray (CXR) modalities. Joint fusion outperformed mono-modality DL techniques, with MobileNetV2, ResNet50V2 and InceptionResNetV2 joint fusion as the best performing for COVID-19 diagnosis with an accuracy of 99%. Therefore, we recommend the use of the joint fusion DL models MobileNetV2, ResNet50V2 and InceptionResNetV2 for the detection of COVID-19. As for monomodality, MobileNetV2 was the best in performance and less sensitive model to the two imaging modalities.