Analysis of chest X-Ray (CXR) images in COVID-19 patients based on age using the Otsu thresholding segmentation method

The infection with the COVID-19 virus or better known as the Corona virus spread throughout China and other countries around the world until it was designated a pandemic by the World Health Organization (WHO). Detection of patients infected with COVID-19 in the form of RT-PCR, CT-Scan images and Chest X-Ray (CXR). This study aims to analyze CXR images of COVID-19 patients based on age using Otsu Thresholding Segmentation. The image segmentation process uses the Otsu auto-tresholding method to separate objects from the background on the CXR image. The results show that the images of COVID-19 patients have pneumonia spots that are not visible on the original CXR image. The average value of the accuracy of the Otsu Thresholding results is 95.18%. Penunomia spots are mostly found in COVID-19 patients aged 50 to 70 years and over which cause severe lung damage.©2021 JNSMR UIN Walisongo. All rights reserved

Analysis of chest X-Ray (CXR) images in COVID-19 patients based on age using the Otsu thresholding segmentation method

The infection with the COVID-19 virus or better known as the Corona virus spread throughout China and other countries around the world until it was designated a pandemic by the World Health Organization (WHO). Detection of patients infected with COVID-19 in the form of RT-PCR, CT-Scan images and Chest X-Ray (CXR). This study aims to analyze CXR images of COVID-19 patients based on age using Otsu Thresholding Segmentation. The image segmentation process uses the Otsu auto-tresholding method to separate objects from the background on the CXR image. The results show that the images of COVID-19 patients have pneumonia spots that are not visible on the original CXR image. The average value of the accuracy of the Otsu Thresholding results is 95.18%. Penunomia spots are mostly found in COVID-19 patients aged 50 to 70 years and over which cause severe lung damage.©2021 JNSMR UIN Walisongo. All rights reserved

The analysis of differences at Binary Image in COVID-19 and ARDS Patients from chest X-Ray examination

Corona virus disease 2019 (COVID-19), a viral infection that was discovered at the end of December 2019 in Wuhan, China. The spread and transmission of this virus is very fast even to all countries in the world. Meanwhile, Acute Respiratory Distress Syndrome (ARDS) is an emergency condition in the field of pulmonology that occurs due to fluid accumulation in the alveoli causes gas exchange disorders so that oxygen distribution to tissues were reduced. In this study, Chest X-Ray (CXR) image processing done in COVID-19 and ARDS patients with the aim of analyzing the differences in binary image using the Otsu Thresholding method. This study prioritizes improving the quality of the original CXR image by segmentation using calculating the Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) values. The results showed that the difference between CXR images in COVID-19 patients and ARDS lies in the extent of spread, in COVID-19 patients the extent of spread varies depending on the length of time the virus has invaded and not all of it starts from the alveolus, while ARDS tends to be constant and starts from the lungs. The lower part of the lung, specifically the alveoli. ©2022 JNSMR UIN Walisongo. All rights reserved

The analysis of differences at Binary Image in COVID-19 and ARDS Patients from chest X-Ray examination

Corona virus disease 2019 (COVID-19), a viral infection that was discovered at the end of December 2019 in Wuhan, China. The spread and transmission of this virus is very fast even to all countries in the world. Meanwhile, Acute Respiratory Distress Syndrome (ARDS) is an emergency condition in the field of pulmonology that occurs due to fluid accumulation in the alveoli causes gas exchange disorders so that oxygen distribution to tissues were reduced. In this study, Chest X-Ray (CXR) image processing done in COVID-19 and ARDS patients with the aim of analyzing the differences in binary image using the Otsu Thresholding method. This study prioritizes improving the quality of the original CXR image by segmentation using calculating the Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) values. The results showed that the difference between CXR images in COVID-19 patients and ARDS lies in the extent of spread, in COVID-19 patients the extent of spread varies depending on the length of time the virus has invaded and not all of it starts from the alveolus, while ARDS tends to be constant and starts from the lungs. The lower part of the lung, specifically the alveoli. ©2022 JNSMR UIN Walisongo. All rights reserved

Segmentation and measurement of lung pathological changes for COVID-19 diagnosis based on computed tomography

Coronavirus 2019 (COVID-19) spread internationally in early 2020, resulting from an existential health disaster. Automatic detecting of pulmonary infections based on computed tomography (CT) images has a huge potential for enhancing the traditional healthcare strategy for treating COVID-19. CT imaging is essential for diagnosis, the process of assessment, and the staging of COVID-19 infection. The detection in association with computed tomography faces many problems, including the high variability, and low density between the infection and normal tissues. Processing is used to solve a variety of diagnostic tasks, including highlighting and contrasting things of interest while taking color-coding into account. In addition, an evaluation is carried out using the relevant criteria for determining the alterations nature and improving a visibility of pathological changes and an accuracy of the X-ray diagnostic report. It is proposed that pre-processing methods for a series of dynamic images be used for these objectives. The lungs are segmented and parts of probable disease are identified using the wavelet transform and the Otsu threshold value. Delta maps and maps created with the Shearlet transform that have contrasting color coding are used to visualize and select features (markers). The efficiency of the suggested combination of approaches for investigating the variability of the internal geometric features (markers) of the object of interest in the photographs is demonstrated by analyzing the experimental and clinical material done in the work. The suggested system indicated that the total average coefficient obtained 97.64% regarding automatic and manual infection sectors, while the Jaccard similarity coefficient achieved 96.73% related to the segmentation of tumor and region infected by COVID-19

DETEKSI COVID-19 PADA CITRA CT-SCAN MENGGUNAKAN K-NEAREST NEIGHBOR

Covid 19 adalah penyakit yang disebabkan oleh virus SARS-CoV-2 yang menyerang organ pada sistem pernafasan manusia, dengan sasaran utama organ paru-paru. Pemeriksaan utama untuk diagnosis COVID 19 adalah melalui tes swab PCR, akan tetapi pemeriksaan tersebut membutuhkan waktu yang relative lama. Sementara itu, pemeriksaan CT Scan dapat menjadi alternatif untuk mendeteksi COVID 19 dengan cepat, karena terdapat ground glass opacity pada citra radiologi paru-paru yang terinfeksi covid 19. Pada penelitian ini dilakukan eksperimen untuk mendeteksi covid 19 pada citra CT Scan dada, menggunakan K-nearest neighbor yang ringan dan efisien, dikombinasikan dengan metode segmentasi paru-paru, serta ekstraksi fitur nilai prosentase area terang dan nilai prosentase area gelap pada citra paru-paru. Akurasi tertinggi yang dicapai pada penelitian adalah 98,44%

Social group optimization–assisted Kapur's entropy and morphological segmentation for automated detection of COVID-19 infection from computed tomography images

The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning–based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19–affected CTI using social group optimization–based Kapur's entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis–based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection