COVID-CAPS: A capsule network-based framework for identification of COVID-19 cases from X-ray images

By Parnian Afshar, Shahin Heidarian, Farnoosh Naderkhani, A. Oikonomou, K. Plataniotis, Arash Mohammadi. Pattern Recognition Letters, 2020

629 citations recorded by the source corpus.

Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID-19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images, on the other hand, reveal specific manifestations associated with this disease. Overlap with other lung infections makes human-centered diagnosis of COVID-19 challenging. Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets. The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID-19. Our results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPS achieved an Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, and Area Under the Curve (AUC) of 0.97, while having far less number of trainable parameters in comparison to its counterparts. To potentially and further improve diagnosis capabilities of the COVID-CAPS, pre-training and transfer learning are utilized based on a new dataset constructed from an external dataset of X-ray images. This is in contrary to existing works on COVID-19 detection where pre-training is performed based on natural images. Pre-training with a dataset of similar nature further improved accuracy to 98.3% and specificity to 98.6%.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/9b8eaad6e6ebf2b480067ce3d923a0344f64e7c6

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Citations in the corpus

COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images (2020, positive): “The deep Bayes-SqueezeNet model catches all the Covid samples in the X-rays, perfectly.”
COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images (2020, methodology): “A brief summary of these works is provided in Table 2 The most related studies to ours as they proposed diﬀerent models to the typical ones are [17] and [19].”
Evaluation of artificial intelligence techniques in disease diagnosis and prediction (2023, background): “In this work, they achieved an accuracy of 95.7% [121].”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, positive): “Since the time series used in this study are also nonlinear and irregular, so it can be said that the results of this study are consistent with previous studies.”
Convolutional capsnet: A novel artificial neural network approach to detect COVID-19 disease from X-ray images using capsule networks (2020, neutral): “While CT images were used in the study of Mobiny et al. [36] , in the study of Afshar et al. [35] , X-ray images and 5 different thorax data sets were examined.”
Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, methodology): “[31] X-ray Capsule Networks 4 95.”
Deep learning based detection and analysis of COVID-19 on chest X-ray images (2020, background): “In [17], the authors proposed a framework model based on Capsule Networks to diagnose Covid-19 (i.e., COVID-CAAPS) disease with the help of X-ray images.”
CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection (2020, neutral): “The last step after the segmentation Wang and Wong [63] Deep Learning 92.30 Afshar, Heidarian, Naderkhani, Oikonomou, Plataniotis and Mohammadi [64] Capsule network 95.70 Chowdhury, Rahman, Khandakar, Mazhar, Kadir, Mahbub, Islam, Khan, Iqbal and Al-Emadi [30] Transfer Learning 97.94 Farooq and…”
Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation (2020, neutral): “[2] the authors opted for this method.”