Lung Disease Classification using GLCM and Deep Features from Different Deep Learning Architectures with Principal Component Analysis

Ming, Joel Than Chia; Noor, Norliza Mohd; Rijal, Omar Mohd; Kassim, Rosminah M.; Ashari, Yunus

doi:10.30880/ijie.2018.10.07.008

Cited by 6 publications

(4 citation statements)

References 29 publications

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“…The previous information of the photos is obtained via GLCM. This texture analysis technique has several applications, one of which being the examination of medical photographs [30]. One by one, we look at each of the surrounding and reference pixels.…”

Section: Texture Feature Extraction Using Gray-level Co-occurrence Ma...mentioning

confidence: 99%

“…The review paper by Sawant & Sreemathy, (2023) [29] demonstrates the SVM model's potential for classifying lung cancer using CT scan images by combining texture features. Using CT scan images, Chia Ming et al (2018) [30] developed a model for lung disease classification that has higher accuracy by combining GLCM texture features with deep learning. It was chosen to leverage several texture features by incorporating them in machine learning methods to construct COVID detection models based on CT scans, after being inspired by these literatures.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient and Non-Invasive Approach to Identify and Classify COVID-19 from CT Scan Images using Machine Learning in Combination with Hybrid Texture Features

kumar

2024

Preprint

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Rapid and accurate identification of COVID-19 and also other associated diseases is now crucial to limiting the disease's transmission, relaxing lockdown laws, and reducing the burden on public health infrastructures. Recently, several approaches and techniques have been proposed to identify the SARS-CoV-2 virus (COVID-19) using different clinical data and medical pictures. There are some limitations and shortcomings with the COVID-19 detection technologies that are currently available on the market. Because of this, it becomes essential to develop and study new diagnostic tools that have higher diagnostic accuracy while avoiding the shortcomings of existing tools. This study used the SARS-CoV-2 CT scan dataset to test non-linear SVM and Twin-SVM (TWSVM) classifiers in addition to textural characteristics such as GLCM, GLRLM, and ILMFD separately. There are a total of 2482 CT scan images in this database; 1252 of the scans show positive signs of SARS-CoV-2 infection (COVID-19), and 1230 show negative signs. Eight different models were developed in this work for the purpose of classifying and predicting COVID-19. We found that the GLCM + NLSVM model using RBF kernal, GLCM + TWSVM using linear kernal, GLRLM + NLSVM using RBF kernal, GLRLM + TWSVM using sigmoid, ILMFD + NLSVM using RBF kernal, ILMFD + TWSVM using polynomial kernal, Hybrid feature + NLSVM, and Hybrid feature + TWSVM all performed better in terms of evaluation done by performance metrics used in this work. For the given dataset, the Hybrid feature + NLSVM model with Linear Kernal yielded significantly better results out of eight models tested, including 100% accuracy, 100% recall, 100% precision, 100% F1-score, R-Squared = 1, and RMSE = 0. As a result, the high accuracy of this type of computer-aided screening method would significantly boost the speed and accuracy of COVID-19 diagnosis also encourage the study of other associated diseases with CT-scan images.

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Section: Texture Feature Extraction Using Gray-level Co-occurrence Ma...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient and Non-Invasive Approach to Identify and Classify COVID-19 from CT Scan Images using Machine Learning in Combination with Hybrid Texture Features

kumar

2024

Preprint

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“…Paper [8] classified lung disease using deep networks and compared the results with the results of GLCM-based classification. The dataset used here consisted of information on 81 infected and 15 normal patients as computed tomography slices of high resolution.…”

Section: Literature Surveymentioning

confidence: 99%

COVID-19 Identification using Machine Learning Classifiers with GLCM Features of Chest X-ray Images

Thepade¹,

Jha²

2021

Trends Sci

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COVID-19 is an ongoing pandemic, and is also known by the name coronavirus. It was originally discovered in Wuhan, China, in December, 2019. Since then, it has been increasing rapidly worldwide. Since it has been increasing at such a rapid pace, testing equipment has limited availability. Also, this disease spreads very quickly, so it is better if it is detected earlier, in order so that it can be stopped from spreading. Therefore, the importance of early detection has increased; however, because of the shortage of testing sets, it is a necessity to develop an automated system that can detect whether the COVID-19 disease is present in a person or not as early as possible. Therefore, in this work, to extract features from X-ray images of the chest, we have made use of the Gray Level Co-occurrence Matrix (GLCM). After extracting these features for the classification of the images, we used different machine learning models, and an ensemble of machine learning models, to classify X-ray images of the chest as COVID-19, Normal, Pneumonia-bac, or Pneumonia-vir. Considering the average of performance metrics, the ensemble of Random Forest-MLP gave the best result among the variations.

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“…Many organizations, including the National Asthma Education and Prevention Program, Global Initiative for Chronic Obstructive Lung Disease (GOLD), and American Thoracic Society (ATS), recommend using these tests [14]. In Malaysia, normally lung disease detect at advance level [15].…”

Section: Introductionmentioning

confidence: 99%

Lung Function Test among Abrasive Blasting Workers in Lumut, Perak

Zakaria

Fauzy²,

Sies³

et al. 2019

IJIE

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Abrasive blasting process is a common process in marine industry. This process was use as main cleaning process method and normally will release fine dust that hazardous to human health. Main effect from fine dust to human health are respiratory failure such as chronic obstructive pulmonary disease (COPD). Lung function test can be used to detect any respiratory problems in human. The objective of the research to evaluate the effect of dust exposure from abrasive blasting to the workers. This study is conduct among workers work in dusty environment in Lumut, Perak, Malaysia. FEV1/FVC value from lung function test were evaluated to determine the severity of COPD among workers. While FEV1 prediction show restrictive pattern and use to determine any respiratory or lung problems among the workers. From the result, it does not show any COPD problem among workers. However, FEV1 show that some workers need to go for detail medical surveillance because of the result is less than 0.8. Employer and workers need to understand and know their work-related risk to minimize the risk arising from their job or work conducted.

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Lung Disease Classification using GLCM and Deep Features from Different Deep Learning Architectures with Principal Component Analysis

Cited by 6 publications

References 29 publications

An Efficient and Non-Invasive Approach to Identify and Classify COVID-19 from CT Scan Images using Machine Learning in Combination with Hybrid Texture Features

An Efficient and Non-Invasive Approach to Identify and Classify COVID-19 from CT Scan Images using Machine Learning in Combination with Hybrid Texture Features

COVID-19 Identification using Machine Learning Classifiers with GLCM Features of Chest X-ray Images

Lung Function Test among Abrasive Blasting Workers in Lumut, Perak

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