[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127328-en":3,"doc-seo-127328-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127328,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",7,"Healthcare","Detecting COVID-19 from chest X-ray images using machine learning and deep convolutional neural networks - Research overview","A novel coronavirus outbreak in December 2019 created urgent demand for early diagnosis, especially for patients lacking clear symptoms. This study proposes an AI vision framework to identify COVID-19 from chest X-ray images by combining classical machine learning models and a deep convolutional neural network. Logistic regression, decision tree, and random forest methods achieve over 95% accuracy, while the proposed deep CNN reaches a 99.99% detection rate, enabling effective early-stage detection.","Indonesian Journal of Electrical Engineering and Computer Science  \nVol. 35, No. 3, September 2024, pp. 1786􀀘 1795  \nISSN: 2502-4752, DOI: 10.11591/ijeecs.v35.i3.pp1786-1795 r 1786  \n\n| Detecting COVID-19 from chest X-ray images using machine learning and deep convolutional neural networks\u003Cbr>Amol D. Vibhute1 , Chandrashekhar H. Patil2 , Jatinderkumar R. Saini1 , Harshali P. Patil2\u003Cbr>1 Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India\u003Cbr>2 School of Computer Science, Dr. Vishwanath Karad MIT World Peace University, Pune, India |  |  |\n| --- | --- | --- |\n| Article Info\u003Cbr>Article history:\u003Cbr>Received Oct 30, 2023 Revised Apr 24, 2024 Accepted May 7, 2024\u003Cbr>Keywords:\u003Cbr>Chest X-ray\u003Cbr>Convolutional neural network COVID-19\u003Cbr>Decision tree Random forest X-ray image |  | ABSTRACT\u003Cbr>The world was affected by a novel coronavirus in December 2019 that changed human life. Several types of research have been done, substantial scientific advances have been made, and millions of dollars have been spent on bringing scholars and scientists to one platform to end this critical pandemic. Ascertaining COVID-19 diagnoses in the initial stage of the pandemic was critical, specifically for patients with no manifestations. In this case, artificial intelligence-based systems were proposed to identify the virus at an earlier phase. Thus, the present study suggests a machine vision scheme to identify COVID-19 from chest X-ray images. Three machine learning approaches, such as logistic regression (LR), decision tree (DT), and random forest (RF), were implemented with more than 95% accuracy. The deep convolutional neural network (CNN) architecture was also proposed and implemented with a 99.99% detection rate. Therefore, the present work can effectively detect COVID-19 cases in the early stages.\u003Cbr>This is an open access article under the CC BY-SA license. |\n| Corresponding Author: |  |  |\n| Chandrashekhar H. Patil\u003Cbr>School of Computer Science, Dr. Vishwanath Karad MIT World Peace University Pune, MH, India\u003Cbr>Email: [chpatil.mca@gmail.com](chpatil.mca@gmail.com) |  |  |\n\n1. INTRODUCTION  \nIn December 2019, the coronavirus hit the globe as the COVID-19 variant. The COVID-19 virus was harmful to humans and severely impacted worldwide, starting from the Wuhan province of China. It is highly infectious and contagious [1],[2] . The COVID-19 was formerly recognized as the SARS-CoV-2 variant of the coronavirus family. The virus was known to spread by being in close contact with the infected person [3] . In this case, significant methods of detecting COVID-19 cases were used, such as reverse transcriptionpolymerase chain reaction (RT-PCR) or gene decoding for respiratory or blood samples [4] . However, scientists have developed vaccines worldwide to help the body pick a fight against the variant virus. However, no adequate medication or vaccine is still available for the COVID-19 disease that protects humans 100% against the virus [5], [6] . COVID-19 has been known to attack the lungs and tissues of the infected person, like Pneumonia [7]–[9] . The research shows that the manifestations of Pneumonia and the COVID-19 variant are highly related, including cough with phlegm or pus, fever, colds, body pains, a headache, and hardship breathing, which can be noticeable with chest X-ray or computed tomography (CT) imaging [10], [11] . However, differentiation between the cause being COVID-19 or Pneumonia bacteria/virus needs to be identified early as the chances for the COVID-19 virus to spread are severely high, which may lead to several deaths as well [12]–[14] . Thus, chest X-ray images are essential to the verdict of the earlier control used globally as a first-line analytical  \nmechanism [15] . The situation of the lungs can be noticed using radioscopy scans together with the various phases of infection or retrieval [16] . Radiotherapists have documented anomalies seen in the chest scans of COV","cbCaiiNceiHOG5UR","https://ap.wps.com/l/cbCaiiNceiHOG5UR","pdf",330344,1,10,"English","en",105,"# Abstract\n# Introduction\n## Background of COVID-19 detection challenges\n## Related work using AI on X-ray and CT imaging","[{\"question\":\"Why is early COVID-19 detection important according to the study?\",\"answer\":\"Early detection is critical for patients with no manifestations, because timely identification reduces the risk of spread and severe outcomes.\"},{\"question\":\"Which machine learning methods were implemented for detecting COVID-19 from chest X-rays?\",\"answer\":\"The study implemented logistic regression, decision tree, and random forest, achieving more than 95% accuracy.\"},{\"question\":\"What performance was achieved by the proposed deep convolutional neural network?\",\"answer\":\"The deep convolutional neural network achieved a 99.99% detection rate for COVID-19 cases from chest X-ray images.\"}]","Detecting COVID-19 from chest X-ray images using machine learning and deep convolutional neural networks - Research overview | PDF",1785938318,25,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"detecting-covid-19-from-chest-x-ray-images-using-machine-learning-and-deep-convolutional-neural-networks-research-overview","",{"@graph":36,"@context":86},[37,54,69],{"@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/detecting-covid-19-from-chest-x-ray-images-using-machine-learning-and-deep-convolutional-neural-networks-research-overview/127328/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-25","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is early COVID-19 detection important according to the study?","Question",{"text":76,"@type":77},"Early detection is critical for patients with no manifestations, because timely identification reduces the risk of spread and severe outcomes.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning methods were implemented for detecting COVID-19 from chest X-rays?",{"text":81,"@type":77},"The study implemented logistic regression, decision tree, and random forest, achieving more than 95% accuracy.",{"name":83,"@type":74,"acceptedAnswer":84},"What performance was achieved by the proposed deep convolutional neural network?",{"text":85,"@type":77},"The deep convolutional neural network achieved a 99.99% detection rate for COVID-19 cases from chest X-ray images.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,119,124,129,132,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":117,"slug":118},40,"healthcare",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},8,"Research & Report",30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":21,"slug":134},"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]