Texture Analysis in the Evaluation of COVID-19 Pneumonia in Chest X-Ray Images: A Proof of Concept Study

Cavallo, Armando Ugo; Troisi, Jacopo; Forcina, Marco; Mari, Pier‐Valerio; Forte, Valerio; Sperandio, Massimiliano; Pagano, S.; Cavallo, Pierpaolo; Floris, Roberto; Garaci, Francesco

doi:10.2174/1573405617999210112195450

Cited by 15 publications

(6 citation statements)

References 38 publications

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“…The proposed method with Res-net 34 achieved 98.33% accuracy. Armando Ugo Cavallo et al [ 13 ] extracted 308 texture features per ROI from chest X-ray and analyzed pneumonia infection in COVID-19 patients using machine-learning models. Metaheuristic algorithms [ 14 ] are becoming more popular because they do not require gradient information, have ability to avoid entrapment into local optima, and are utilized to solve complex optimization problems efficiently.…”

Section: Introductionmentioning

confidence: 99%

Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm

Shivahare

Gupta²

2022

Journal of Healthcare Engineering

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This article addresses automated segmentation and classification of COVID-19 and normal chest CT scan images. Segmentation is the preprocessing step for classification, and 12 DWT-PCA-based texture features extracted from the segmented image are utilized as input for the random forest machine-learning algorithm to classify COVID-19/non-COVID-19 disease. Diagnosing COVID-19 disease through an RT-PCR test is a time-consuming process. Sometimes, the RT-PCR test result is not accurate; that is, it has a false negative, which can cause a threat to the person’s life due to delay in starting the specified treatment. At this moment, there is an urgent need to develop a reliable automatic COVID-19 detection tool that can detect COVID-19 disease from chest CT scan images within a shorter period and can help doctors to start COVID-19 treatment at the earliest. In this article, a variant of the whale optimization algorithm named improved whale optimization algorithm (IWOA) is introduced. The efficiency of the IWOA is tested for unimodal (F1–F7), multimodal (F8–F13), and fixed-dimension multimodal (F14–F23) benchmark functions and is compared with the whale optimization algorithm (WOA), salp swarm optimization (SSA), and sine cosine algorithm (SCA). The experiment is carried out in 30 trials and population size, and iterations are set as 30 and 100 under each trial. IWOA achieves faster convergence than WOA, SSA, and SCA and enhances the exploitation and exploration phases of WOA, avoiding local entrapment. IWOA, WOA, SSA, and SCA utilized Otsu’s maximum between-class variance criteria as fitness function to compute optimal threshold values for multilevel medical CT scan image segmentation. Evaluation measures such as accuracy, specificity, precision, recall, Gmean, F_measure, SSIM, and 12 DWT-PCA-based texture features are computed. The experiment showed that the IWOA is efficient and achieved better segmentation evaluation measures and better segmentation mask in comparison with other methods. DWT-PCA-based texture features extracted from each of the 160 IWOA-, WOA-, SSA-, and SCA-based segmented images are fed into random forest for training, and random forest is tested with DWT-PCA-based texture features extracted from each of the 40 IWOA-, WOA-, SSA-, and SCA-based segmented images. Random forest has reported a promising classification accuracy of 97.49% for the DWT-PCA-based texture features, which are extracted from IWOA-based segmented images.

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Section: Introductionmentioning

confidence: 99%

Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm

Shivahare

Gupta²

2022

Journal of Healthcare Engineering

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“…Models showing an accuracy higher of 60% were selected and ensembled using a voting scheme assuming both cross-validation accuracy and confidence (i.e., distance from classification margin) as a vote weight. Ensembling was performed according to Cavallo et al [ 12 ]. Briefly, for the images classified as derived from subjects with Hypertension (HTN), the scores (the products of model accuracy and classification confidences) were left as is, while for each Controls (NC) classification the scores were multiplied by −1.…”

Section: Methodsmentioning

confidence: 99%

Cardiac Computed Tomography Radiomics-Based Approach for the Detection of Left Ventricular Remodeling in Patients with Arterial Hypertension

et al. 2022

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The aim of the study is to verify the feasibility of a radiomics based approach for the detection of LV remodeling in patients with arterial hypertension. Cardiac Computed Tomography (CCT) and clinical data of patients with and without history of arterial hypertension were collected. In one image per patient, on a 4-chamber view, left ventricle (LV) was segmented using a polygonal region of interest by two radiologists in consensus. A total of 377 radiomics features per region of interest were extracted. After dataset splitting (70:30 ratio), eleven classification models were tested for the discrimination of patients with and without arterial hypertension based on radiomics data. An Ensemble Machine Learning (EML) score was calculated from models with an accuracy >60%. Boruta algorithm was used to extract radiomic features discriminating between patients with and without history of hypertension. Pearson correlation coefficient was used to assess correlation between EML score and septum width in patients included in the test set. EML showed an accuracy, sensitivity and specificity of 0.7. Correlation between EML score and LV septum width was 0.53 (p-value < 0.0001). We considered LV septum width as a surrogate of myocardial remodeling in our population, and this is the reason why we can consider the EML score as a possible tool to evaluate myocardial remodeling. A CCT-based radiomic approach for the identification of LV remodeling is possible in patients with past medical history of arterial hypertension.

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“… Comparative study between proposed model and other literature models using X-ray dataset (three-class) [ 51 , 52 , 53 , 54 ]. …”

Section: Figurementioning

confidence: 99%

Multisource Smart Computer-Aided System for Mining COVID-19 Infection Data

et al. 2022

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In this paper, we approach the problem of detecting and diagnosing COVID-19 infections using multisource scan images including CT and X-ray scans to assist the healthcare system during the COVID-19 pandemic. Here, a computer-aided diagnosis (CAD) system is proposed that utilizes analysis of the CT or X-ray to diagnose the impact of damage in the respiratory system per infected case. The CAD was utilized and optimized by hyper-parameters for shallow learning, e.g., SVM and deep learning. For the deep learning, mini-batch stochastic gradient descent was used to overcome fitting problems during transfer learning. The optimal parameter list values were found using the naïve Bayes technique. Our contributions are (i) a comparison among the detection rates of pre-trained CNN models, (ii) a suggested hybrid deep learning with shallow machine learning, (iii) an extensive analysis of the results of COVID-19 transition and informative conclusions through developing various transfer techniques, and (iv) a comparison of the accuracy of the previous models with the systems of the present study. The effectiveness of the proposed CAD is demonstrated using three datasets, either using an intense learning model as a fully end-to-end solution or using a hybrid deep learning model. Six experiments were designed to illustrate the superior performance of our suggested CAD when compared to other similar approaches. Our system achieves 99.94, 99.6, 100, 97.41, 99.23, and 98.94 accuracy for binary and three-class labels for the CT and two CXR datasets.

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Texture Analysis in the Evaluation of COVID-19 Pneumonia in Chest X-Ray Images: A Proof of Concept Study

Cited by 15 publications

References 38 publications

Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm

Efficient COVID-19 CT Scan Image Segmentation by Automatic Clustering Algorithm

Cardiac Computed Tomography Radiomics-Based Approach for the Detection of Left Ventricular Remodeling in Patients with Arterial Hypertension

Multisource Smart Computer-Aided System for Mining COVID-19 Infection Data

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