Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia

Abstract

Coronavirus disease (COVID-19) has created an unprecedented devastation and the loss of millions of lives globally. Contagious nature and fatalities invariably pose challenges to physicians and healthcare support systems. Clinical diagnostic evaluation using reverse transcription-polymerase chain reaction and other approaches are currently in use. The Chest X-ray (CXR) and CT images were effectively utilized in screening purposes that could provide relevant data on localized regions affected by the infection. A step towards automated screening and diagnosis using CXR and CT could be of considerable importance in these turbulent times. The main objective is to probe a simple threshold-based segmentation approach to identify possible infection regions in CXR images and investigate intensity-based, wavelet transform (WT)-based, and Laws based texture features with statistical measures. Further feature selection strategy using Random Forest (RF) then selected features used to create Machine Learning (ML) representation with Support Vector Machine (SVM) and a Random Forest (RF) to make different COVID-19 from viral pneumonia (VP). The results obtained clearly indicate that the intensity and WT-based features vary in the two pathologies that are better differentiated with the combined features trained using SVM and RF classifiers. Classifier performance measures like an Area Under the Curve (AUC) of 0.97 and by and large classification accuracy of 0.9 using the RF model clearly indicate that the methodology implemented is useful in characterizing COVID-19 and Viral Pneumonia.

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

“…The median filter was chosen such that the edge-related details were preserved. The images were subjected to multi-threshold segmentation using the Otsu method to obtain three binary masks from the motivation derived from the work carried out [69] primarily due to fewer approaches available in the literature to precisely delineate the diseased region. Hence, this segmentation based approach is utilized here in order to investigate the veracity of the vesselness feature to categorize COVID-19 from other conditions.…”

Automatic COVID-19 and Common-Acquired Pneumonia Diagnosis Using Chest CT Scans

“…Some works have proposed different methods to detect COVID-19 using X-ray images. For example, while Ohata et al [12] and Basha et al [13] used machine learning methods for feature extraction and classification, Hu et al [14] employed transfer learning and pretrained models. Despite the promising results obtained by these studies, some limitations could still be addressed.…”

“…In various medical applications, deep learning models have been used in diagnosis and prognosis tasks. Some works focused on developing deep learning models for accurate disease diagnosis [12][13][14]16,18,24,26]. In contrast, other works focused on predicting the prognosis of a disease [17,23,25,27], such as estimating the likelihood of survival or disease progression.…”