Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

Explainable Analysis of Deep Learning Models for Coronavirus Disease (COVID-19) Classification with Chest X-Ray Images: Towards Practical Applications

In recent years, with numerous developments of convolutional neural network (CNN) classification models for medical diagnosis, the issue of misrecognition/misclassification has become more and more important. Thus, research on misrecognition/misclassification has been progressing. This study focuses on the problem of misrecognition/misclassification of CNN classification models for coronavirus disease (COVID-19) using chest X-ray images. We construct two models for COVID-19 pneumonia classification by fine-tuning ResNet-50 architecture, i.e., a model retrained with full-sized original images and a model retrained with segmented images. The present study demonstrates the uncertainty (misrecognition/misclassification) of model performance caused by the discrepancy in the shapes of images at the phase of model construction and that of clinical applications. To achieve it, we apply three XAI methods to demonstrate and explain the uncertainty of classification results obtained from the two constructed models assuming for clinical applications. Experimental results indicate that the performance of classification models cannot be maintained when the type of constructed model and the geometric shape of input images are not matched, which may bring about misrecognition in clinical applications. We also notice that the effect of adversarial attack might be induced if the method of image segmentation is not performed properly. The results suggest that the best approach to obtaining a highly reliable prediction in the classification of COVID-19 pneumonia is to construct a model using full-sized original images as training data and use full-sized original images as the input when utilized in clinical applications.