[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123873-en":3,"doc-seo-123873-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123873,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",7,"Healthcare","Machine learning model based on Gary-level co-occurrence matrix for chest Sarcoidosis diagnosis - read online free","Sarcoidosis is frequently misdiagnosed and mistreated because radiological presentations are limited, making clinical differentiation from COVID-19 especially difficult. Similarity in symptoms complicates timely diagnosis and can adversely affect patient outcomes. The diagnostic workflow is time-consuming, labor-intensive, and costly, motivating computer-aided detection. A machine learning approach combines classifiers, ensembles, and Gray-Level Co-occurrence Matrix (GLCM) texture plus histogram features on chest X-ray images. Multi-class labeling separates sarcoidosis-affected, COVID-19-affected, and normal lungs, alongside binary sarcoid-versus-others classification, demonstrating efficient and accurate performance.","Machine learning model based on Gary-level co-occurrence matrix for chest Sarcoidosis diagnosis  \nAttar, Hani; Solyman, Ahmed; Deif, Mohanad A. ; Hafez, Mohamed; Kasem, Hager M. ; Mohamed, Abd-Elnaser Fawzy  \nPublished in:  \n2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI)  \nDOI:  \n10.1109/EICEEAI60672.2023.10590168  \nPublication date:  \n2024  \nDocument Version  \nAuthor accepted manuscript  \nLink to publication in ResearchOnline  \nCitation for published version (Harvard):  \nAttar, H, Solyman, A, Deif, MA, Hafez, M, Kasem, HM & Mohamed, A-EF 2024, Machine learning model based on Gary-level co-occurrence matrix for chest Sarcoidosis [diagnosis. in](diagnosis. in) 2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI). International Engineering Conference on Electrical, Energy, and Artificial Intelligence, IEEE, 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, Zarqa, Jordan, 27/12/23 .  \n[https://doi.org/10.1109/EICEEAI60672.2023.10590168](https://doi.org/10.1109/EICEEAI60672.2023.10590168)  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \nTake down policy  \nIf you believe that this document breaches copyright please view our takedown policy at [https://edshare.gcu.ac.uk/id/eprint/5179 for details](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[ ](https://edshare.gcu.ac.uk/id/eprint/5179 for details)[of how to contact us.](of how to contact us.)  \nDownload date: 09. Sep. 2024  \nMachine Learning Model Based on Gary-Level Cooccurrence Matrix for Chest Sarcoidosis Diagnosis  \nHani Attar  \nFaculty of Engineering, Zarqa University, Zarqa, Jordan College of Engineering University of Business and Technology Jeddah, Saudi Arabia [Hattar@zu.edu.jo](Hattar@zu.edu.jo)  \nMohamed Hafez Faculty of Engineering FEQSINTI-IU-University Nilai, Malaysia [mohdahmed.hafez@newinti.edu.my](mohdahmed.hafez@newinti.edu.my)  \nAhmed Solyman  \nSchool of Computing, Engineering and Built Environment  \nGlasgow Caledonian University Glasgow, UK [ahmed.solyman@gcu.ac.uk](ahmed.solyman@gcu.ac.uk)  \nHager M. Kasem Department of Bioelectronics Modern University of Technology and Information (MTI) University Cairo, Egypt [HAJAR.89898@eng.mti.edu.eg](HAJAR.89898@eng.mti.edu.eg)  \nMohanad A. Deif Department of Artificial Intelligence, College of Information Technology Misr University for Science & Technology (MUST) October 6 City 12566, Egypt [Mohanad.Deif@must.edu.eg](Mohanad.Deif@must.edu.eg)  \n[Abd-Elnaser Fawzy Mohamed](Abd-Elnaser Fawzy Mohamed)[ ](Abd-Elnaser Fawzy Mohamed)Communication Department Bilbies Higher Institute for Engineering Sharqia, Egypt [dr_naser62@yahoo.com](dr_naser62@yahoo.com)  \nAbstract—Sarcoidosis is often misdiagnosed and mistreated due to the limitations of radiological presentations. With the recent emergence of COVID-19, doctors face challenges distinguishing between the symptoms of these two diseases. As a result, people are adapting to new practices such as working from home, wearing masks, and using disinfectants. The similarity in symptoms between sarcoidosis and COVID-19 has made it difficult to differentiate between the two conditions, potentially impacting patient outcomes. The diagnostic process for distinguishing between them is time-consuming, laborintensive, and costly. Researchers and medical practitioners have gained significant attention to computer-aided detection (CAD) systems for sarcoidosis using radiological images to address this issue. This study uses machine learning classifiers, ensembles, and features such as Gray-Level Co-occurrence Matrix (GLCM) and histogram analysis to identify lung sarcoidosis infection fro","cbCaihpky7Y6tNx3","https://ap.wps.com/l/cbCaihpky7Y6tNx3","pdf",464245,1,9,"English","en",105,"# Abstract\n# Introduction\n## Background and clinical challenge\n## Research motivation and CAD systems\n# Method Overview\n## Feature extraction with GLCM\n## Model training and classification setup\n# Experimental Focus\n## Multi-class and binary classification tasks\n## Accuracy and efficiency outcomes","[{\"question\":\"Why is diagnosing sarcoidosis difficult compared with COVID-19?\",\"answer\":\"Radiological presentations for sarcoidosis are limited, and symptoms overlap with COVID-19. This makes differentiation challenging and can impact patient outcomes.\"},{\"question\":\"What features does the proposed method use from chest X-ray images?\",\"answer\":\"The method extracts statistical texture features by computing a Gray-Level Co-occurrence Matrix (GLCM) for each image using different stride combinations, and it also leverages histogram analysis.\"},{\"question\":\"How does the study structure its classification tasks?\",\"answer\":\"It performs multi-class classification into three categories—sarcoidosis-affected, COVID-19-affected, and regular lungs—and also runs a binary classification distinguishing sarcoid-affected cases from others.\"}]","Machine learning model based on Gary-level co-occurrence matrix for chest Sarcoidosis diagnosis - read online free | PDF",1785819006,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-model-based-on-gary-level-co-occurrence-matrix-for-chest-sarcoidosis-diagnosis-read-online-free","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-model-based-on-gary-level-co-occurrence-matrix-for-chest-sarcoidosis-diagnosis-read-online-free/123873/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is diagnosing sarcoidosis difficult compared with COVID-19?","Question",{"text":75,"@type":76},"Radiological presentations for sarcoidosis are limited, and symptoms overlap with COVID-19. This makes differentiation challenging and can impact patient outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What features does the proposed method use from chest X-ray images?",{"text":80,"@type":76},"The method extracts statistical texture features by computing a Gray-Level Co-occurrence Matrix (GLCM) for each image using different stride combinations, and it also leverages histogram analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study structure its classification tasks?",{"text":84,"@type":76},"It performs multi-class classification into three categories—sarcoidosis-affected, COVID-19-affected, and regular lungs—and also runs a binary classification distinguishing sarcoid-affected cases from others.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]