Automatic diagnosis of severity of COVID-19 patients using an ensemble of transfer learning models with convolutional neural networks in CT images

Abstract

Introduction: Quantification of lung involvement in COVID-19 using chest Computed tomography (CT) scan can help physicians to evaluate the progression of the disease or treatment response. This paper presents an automatic deep transfer learning ensemble based on pre-trained convolutional neural networks (CNNs) to determine the severity of COVID -19 as normal, mild, moderate, and severe based on the images of the lungs CT. Material and methods: In this study, two different deep transfer learning strategies were used. In the first procedure, features were extracted from fifteen pre-trained CNNs architectures and then fed into a support vector machine (SVM) classifier. In the second procedure, the pre-trained CNNs were fine-tuned using the chest CT images, and then features were extracted for the purpose of classification by the softmax layer. Finally, an ensemble method was developed based on majority voting of the deep learning outputs to increase the performance of the recognition on each of the two strategies. A dataset of CT scans was collected and then labeled as normal (314), mild (262), moderate (72), and severe (35) for COVID-19 by the consensus of two highly qualified radiologists. Results: The ensemble of five deep transfer learning outputs named EfficientNetB3, EfficientNetB4, InceptionV3, NasNetMobile, and ResNext50 in the second strategy has better results than the first strategy and also the individual deep transfer learning models in diagnosing the severity of COVID-19 with 85% accuracy. Conclusions: Our proposed study is well suited for quantifying lung involvement of COVID-19 and can help physicians to monitor the progression of the disease.

An Intelligent Collaborative Image-Sensing System for Disease Detection

“…The study also revealed the importance of multi-modal data where the authors gathered the medical data from different sources including tomography and the X-ray images. Shalbaf et al [25] implemented 15 pre-trained deep learning models to automatically identify the COVID-19. These models are based on three well-known classification based architecture including Inception, ResNet, and DenseNet.…”

“…The next experiments target validating the usability of the suggested ALMOST framework for disease detection. To reach this conclusion, intensive analysis has been carried out by comparing ALMOST with the baseline solutions InceptionResNet [23], and DenseNet [25]). The detailed results with complete explanation will be shown in the following.…”

Ultrasound Image Analysis with Vision Transformers—Review

“…Artificial intelligence (AI) methods, particularly deep learning models, have brought about ultrasound imaging by automating image processing and enabling the automated diagnosis of disease and detection of abnormalities. Convolutional neural networks (CNNs) have played a vital role in this transformation, demonstrating improvements in various medical imaging modalities [ 4 , 5 , 6 , 7 , 8 , 9 ]. However, the limitations of CNNs in capturing long-range dependencies and contextual information led to the development of vision transformers (ViTs) [ 10 ] in image processing.…”