Using fused Contourlet transform and neural features to spot COVID19 infections in CT scan images

Abstract

The World Health Organization (WHO) claims that COVID19 is the pandemic disease of the 22nd century. The COVID19 disease is caused by a strain of coronavirus that led to the infection and death of millions of people and continues to do so unless we find mechanisms that enable healthcare providers to detect infections accurately and as early as possible. To that end, and to diagnose this lung infection, where CT scan images are usually reliable tools that physicians typically use to spot infections. Like many other research studies in the computing field, we present here a new approach for automating the process of identifying COVID19 infections in CT scans using Machine Learning. This approach uses the hybrid fast fuzzy c-means for COVID19 CT scan image segmentation. Then, the Contourlet transform and CNN feature extracted approaches are used to extract features individually from segmented CT scan images and combine them in one feature vector. For feature selection, we experimented with three feature selection techniques, namely, Principle Component Analysis (PCA), Minimum Redundancy Maximum Relevance (MRMR), and Binary Differential Evaluation (BDE), where we found the latter gave the best results. For classification, we used several neural network models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier worked better. An extensive set of experiments was conducted on standard public datasets. The results suggest that our methodology gives better performance than other existing approaches with an accuracy of 99.98%.

Keywords

• Convolutional neural network (CNN)
• Contourlet transform (CT)
• Computerized tomography (CT)
• Principal component analysis (PCA)
• Minimum redundancy maximum relevance (MRMR)
• Binary differential evaluation (BDE)