Detail-Oriented Capsule Network for classification of CT scan images performing the detection of COVID-19

Abstract

COVID-19 is one of the biggest pandemics that the world is facing today, and every day, we are coming up with new challenges in this area. Still, much research is already going on to overcome this pandemic, and we also get succeeded to some extent. Diverse sources such as MRI, CT scanning, blood samples, X-ray image, and many more are available to detect COVID-19. Thus, it can be easily said that through image processing, the classification of COVID-19 can be done. In this study, the COVID-19 detection is done by classifying with the use of a type of convolutional neural network termed a detail-oriented capsule network. Chest CT scan imaging for the prediction of COVID-19 and non-COVID-19 are classified in the present paper using a Detailed Oriented capsule network (DOCN). Accuracy, specificity, and sensitivity are parameters used for model evaluation. The proposed model has achieved 98% accuracy, 81% sensitivity, and 98.4% specificity.

Comparison of Proposed DCNN Model With Standard CNN Architectures for Retinal Diseases Classification

“…13 Max pooling has been used in the MIDNET18 to emphasize the advantage of considering brighter pixels. [14][15][16][17][18] The proposed method does not favor average pooling since it smoothens the pixels in images and reduces the potential of predicting abnormalities in images. The proposed model additionally employs batch normalization, which aids in avoiding model overfitting and also assists each layer in learning more independently.…”

“…The model makes use of a 224 × 224 input image.The model contains seven max-pooling layers with a pooling size of 2 13. Max pooling has been used in the MIDNET18 to emphasize the advantage of considering brighter pixels [14][15][16][17][18]. The proposed method does not favor average pooling since it smoothens the pixels in images and reduces the potential of predicting abnormalities in images.…”