Detection of COVID-19 disease from X-ray images using capsule-based network




Ashtiani Haghighi, Donya

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The coronavirus (COVID-19) disease has spread abruptly all over the world since the end of 2019. The rapid and accurate diagnosis of COVID-19 is crucial for a better prognosis of this disease and breaking the chain of transition and flattening the epidemic curve. There are different types of COVID-19 diagnosis tests which sometimes have relatively low sensitivity. Computed tomography (CT) scans and X-ray images are other methods for the detection of this disease. However, one of the challenges of using these human-centered diagnosis methods is the overlap with other lung infections. Motivated by this challenge, different Deep Neural Network (DNN)-based diagnosis solutions have been developed, mainly based on Convolutional Neural Networks (CNNs), to accelerate the identification of COVID-19 cases. However, CNN's lose spatial information between image instances and require large datasets. In this project, an alternative framework based on Capsule Networks and Convolutional Neural networks is used which is able to handle small datasets. In addition, by investigating different parameters, the lowest loss of 0.0092, best accuracy of 0.9885, f1 score of 0.9883, the precision of 0.9859, recall of 0.9908, and Area Under the Curve (AUC) of 0.9948 is achieved when Plateau learning rate scheduler and margin loss function is used in capsule network. On the other hand, different dropout rates are used to decrease overfitting, and the dropout rate of 0.1 shows better results. In the last part, by removing one capsule layer and having far less number of trainable parameters 146,752 in comparison to the main architecture, it still shows promising results.



Capsule network, Covid 19