SOM-LWL method for identification of COVID-19 on chest X-rays

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) has had an immense impact on world health and daily life in many countries. Sturdy observing of the initial site of infection in patients is crucial to gain control in the struggle with COVID-19. The early automated detection of the recent coronavirus disease (COVID-19) will help to limit its dissemination worldwide. Many initial studies have focused on the identification of the genetic material of coronavirus and have a poor detection rate for long-term surgery. The first imaging procedure that played an important role in COVID-19 treatment was the chest X-ray. Radiological imaging is often used as a method that emphasizes the performance of chest X-rays. Recent findings indicate the presence of COVID-19 in patients with irregular findings on chest X-rays. There are many reports on this topic that include machine learning strategies for the identification of COVID-19 using chest X-rays. Other current studies have used non-public datasets and complex artificial intelligence (AI) systems. In our research, we suggested a new COVID-19 identification technique based on the locality-weighted learning and self-organization map (LWL-SOM) strategy for detecting and capturing COVID-19 cases. We first grouped images from chest X-ray datasets based on their similar features in different clusters using the SOM strategy in order to discriminate between the COVID-19 and non-COVID-19 cases. Then, we built our intelligent learning model based on the LWL algorithm to diagnose and detect COVID-19 cases. The proposed SOM-LWL model improved the correlation coefficient performance results between the Covid19, no-finding, and pneumonia cases; pneumonia and no-finding cases; Covid19 and pneumonia cases; and Covid19 and no-finding cases from 0.9613 to 0.9788, 0.6113 to 1 0.8783 to 0.9999, and 0.8894 to 1, respectively. The proposed LWL-SOM had better results for discriminating COVID-19 and non-COVID-19 patients than the current machine learning-based solutions using AI evaluation measures.

Coronavirus Detection Using Two Step-AS Clustering and Ensemble Neural Network Model

“…Based on the properties of the X-ray imaging collection, such as correlation, homogeneity, energy and contrast, we chose the four most important Haralick texture elements for further study. The formulas for computing the statistical and GLCM features are discussed and presented by Osman et al [37].…”

Biomarcadores Radiômicos de Precisão: uma breve introdução, alguns desenvolvimentos técnicos e várias aplicações clínicas

“…where S(t − 1) is the likelihood in a previous instant to t, N r is the number of patients at risk in the study excluding the censored cases in the instant t − 1, and N s is the number of patients that survived until the instant t. the lungs and airways. The earliest possible diagnosis of covid-19 is imperative for the patient's isolation to prevent virus spread and for rapid treatment decisions to improve the patient's prognosis [Greenhalgh et al 2020, Osman et al 2021.…”

Morphological characteristics of X-ray thorax images of COVID-19 patients using the Bradley thresholding segmentation

“…We used a standard thorax model from Ercleve [24] as a reference to determine thoracic abnormalities in COVID-19 sufferers. Several other research that use thorax imagery in identifying COVID-19 are the use of the internet of things (IoT) [25], adaptive stratification [26], social approachment [27], blood cell ratio [28], thoraxic surgery [29], tomography images [30], otolaryngology surgery [31], handheld optical system [32], SOM-LWL method [33], therapeutic effects [34], upper respiratory tract [35].…”