COVID-19 and Viral Pneumonia Classification Using Radiomic Features and Deep Learning

The new coronavirus has become the greatest challenge of the 21st century. But since the first cases, much is being discovered about the disease and its effects on the body. Medical imaging, such as X-Rays and CT is widely used to visualize and follow up the patient's clinical picture, especially the effects on the lungs. Although useful, the analysis of this type of image requires some expertise from the radiologist. In less developed countries, the amount of radiologists specialized in chest X-Rays is inadequate, which motivates the development of new technologies to assist clinicians to provide reliable diagnoses. Therefore, this paper addresses the development of a computer-based method to assist in COVID-19 detection among viral pneumonia and health patients through X-Rays images. The proposed method is based on extracting radiomic features and analyzing them using Deep Neural Networks. Experiments following K-Fold Cross-Validation achieved an overall accuracy of 94.98%, a sensibility of 94.89% and an AUC of 99.20%. A benchmark with traditional machine learning algorithms and a binary assessment are also provided. From a multiclass perspective, the analysis and differentiation of COVID-19 and other viral pneumonia reached great results and may assist radiologists in better diagnosing the disease worldwide. © 2022 IEEE.

A Novel CNN-Based Approach for Distinguishing Between COVID and Common Pneumonia

It is well known that the symptoms of Coronavirus disease (COVID) and common pneumonia (CP) disease are very similar though the first one often leads to severe complications and may even be fatal. Hence, it is of vital importance to be able to correctly distinguish between the two. This paper attempts to achieve this task using whole 3-D CT scans of lungs. A number of models have been experimented with, using convolutional and radiomic features as well as their concatenations, and different classifiers (MLP and Random Forest) with two different sizes of input CT images (50 × 128 × 128 and 25 × 256 × 256 ) and their performances have been compared. The most significant contribution of this work is the postulation of a 3-D dual-scale framework using CT scans, employing both intra-scale and inter-scale information, thereby achieving performance scores which are much higher than the state of the art methods to distinguish between COVID-19 and CP using lung CT scans. Specifically, Accuracy of 98.67% and Receiver Operating Characteristics-Area Under The Curve (AUC) of 99% are worth mentioning. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Machine Learning Model Based on Radiomic Features for Differentiation between COVID-19 and Pneumonia on Chest X-ray.

Machine learning approaches are employed to analyze differences in real-time reverse transcription polymerase chain reaction scans to differentiate between COVID-19 and pneumonia. However, these methods suffer from large training data requirements, unreliable images, and uncertain clinical diagnosis. Thus, in this paper, we used a machine learning model to differentiate between COVID-19 and pneumonia via radiomic features using a bias-minimized dataset of chest X-ray scans. We used logistic regression (LR), naive Bayes (NB), support vector machine (SVM), k-nearest neighbor (KNN), bagging, random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM) to differentiate between COVID-19 and pneumonia based on training data. Further, we used a grid search to determine optimal hyperparameters for each machine learning model and 5-fold cross-validation to prevent overfitting. The identification performances of COVID-19 and pneumonia were compared with separately constructed test data for four machine learning models trained using the maximum probability, contrast, and difference variance of the gray level co-occurrence matrix (GLCM), and the skewness as input variables. The LGBM and bagging model showed the highest and lowest performances; the GLCM difference variance showed a high overall effect in all models. Thus, we confirmed that the radiomic features in chest X-rays can be used as indicators to differentiate between COVID-19 and pneumonia using machine learning.

Classification of COVID-19 using Deep Learning and Radiomic Texture Features extracted from CT scans of Patients Lungs

COVID-19 is an air-borne viral infection, which infects the respiratory system in the human body, and it became a global pandemic in early March 2020. The damage caused by the COVID-19 disease in a human lung region can be identified using Computed Tomography (CT) scans. We present a novel approach in classifying COVID-19 infection and normal patients using a Random Forest (RF) model to train on a combination of Deep Learning (DL) features and Radiomic texture features extracted from CT scans of patient's lungs. We developed and trained DL models using CNN architectures for extracting DL features. The Radiomic texture features are calculated using CT scans and its associated infection masks. In this work, we claim that the RFs classification using the DL features in conjunction with Radiomic texture features enhances prediction performance. The experiment results show that our proposed models achieve a higher True Positive rate with the average Area Under the Receiver Curve (AUC) of 0.9768, 95% Confidence Interval (CI) [0.9757, 0.9780]. © 2021 IEEE.