Graph and Capsule Convolutional Neural Network Based Classification of Lung Cancer, Pneumonia, COVID-19 using Lung CT and Ultrasound Radiography Imaging

ABSTRACT

Pulmonary / Lung nodules are a sign of lung cancer. Pneumonia, Lung nodules show up on imaging scans like X-rays, CT or ultrasound scans. The healthcare team may refer to the growth as a spot on the lung, coin lesion, or shadow. Coronavirus (COVID-19) has been identified as a worldwide epidemic, affecting individuals all over the nation. It is vital to identify COVID-19-affected persons to limit the virus's spread. According to the latest study, radiographic approaches can be used to diagnose contamination utilizing deep learning (DL) methods. Considering that DL is a valuable approach and methodology for image analysis, various studies on COVID-19 case detection utilizing radiographs to train DL networks have been conducted. Although just a handful of studies presume to have excellent prediction results, their proposed systems may suffer from a restricted amount of data. Employing graph and capsule, Convolutional Neural Network (CNN) can overcome the shortcomings by predicting multiple disorders using a single network implemented in a hospital. We present a novel comparative method that has paved the way for an open-source COVID-19 case classification approach based on graph and capsule images with CT and ultrasound. Experimental results show that the Capsule network attained the best 98.93% AUC, 99.2% accuracy, 98.4% Fl-score, 98.40% sensitivity, 98.40% specificity, 9S.4l% precision using CT labels. Whereas the ultrasound test set the graph network performed well with 96.93% AUC, 97.26% accuracy, 95.92% Fl-score, 95.90% sensitivity, 97.94% specificity, 96.08% precision. © 2022 IEEE.