[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124067-en":3,"doc-seo-124067-105":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},124067,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Evolution of Machine Learning in Tuberculosis Diagnosis - A Review of Deep Learning-Based Medical Applications","Tuberculosis (TB) is a major global infectious disease causing millions of deaths annually, making timely diagnosis and effective treatment essential for patient recovery. Computer-aided diagnosis (CAD) and machine learning approaches are reviewed as promising tools for supporting TB detection. The review summarizes limitations of conventional TB diagnostics and explains key machine learning algorithms applied to TB. It also discusses deep learning methods combined with neuro-fuzzy logic, genetic algorithms, and artificial immune systems, and highlights state-of-the-art tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest.","electronics   \nReview  \nEvolution of Machine Learning in Tuberculosis Diagnosis: AReview of Deep Learning-Based Medical Applications  \nManisha Singh 1, Gurubasavaraj Veeranna Pujar 1, *, Sethu Arun Kumar 1, Meduri Bhagyalalitha 1, Handattu Shankaranarayana Akshatha 1, Belal Abuhaija 2, *, Anas Ratib Alsoud 3, Laith Abualigah 3,4, Narasimha M. Beeraka 5,6 and Amir H. Gandomi 7, *  \nCitation: Singh, M.; Pujar, G.V.; Kumar, S.A.; Bhagyalalitha, M.; Akshatha, H.S.; Abuhaija, B.; Alsoud, A.R.; Abualigah, L.; Beeraka, N.M.; Gandomi, A.H. Evolution of Machine Learning in Tuberculosis Diagnosis: A Review of Deep Learning-Based Medical Applications. Electronics 2022, 11, 2634. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/electronics11172634](10.3390/electronics11172634)  \nAcademic Editor: Rashid Mehmood  \nReceived: 7 July 2022  \nAccepted: 12 August 2022  \nPublished: 23 August 2022  \nPublisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  \nCopyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education and Research, Sri Shivarathreeshwara Nagara, Mysuru 570015, India  \n2 Department of Computer Science, Wenzhou—Kean University, Wenzhou 325015, China  \n3 Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan  \n4 Faculty of Information Technology, Middle East University, Amman 11831, Jordan  \n5 Department of Human Anatomy, I.M. Sechenov First Moscow State Medical University (Sechenov University), 8/2 Trubetskaya Street, 119991 Moscow, Russia  \n6 Center of Excellence in Molecular Biology and Regenerative Medicine (CEMR), Department of Biochemistry, JSS Academy of Higher Education and Research (JSS AHER), Mysuru 570015, India  \n7 Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia  \n* Correspondence: [gvpujar@jssuni.edu.in](gvpujar@jssuni.edu.in) (G.V.P.); [babuhaij@kean.edu](babuhaij@kean.edu) (B.A.); [gandomi@uts.edu.au](gandomi@uts.edu.au) (A.H.G.)  \nAbstract: Tuberculosis (TB) is an infectious disease that has been a major menace to human health globally, causing millions of deaths yearly. Well-timed diagnosis and treatment are an arch to full recovery of the patient. Computer-aided diagnosis (CAD) has been a hopeful choice for TB diagnosis. Many CAD approaches using machine learning have been applied for TB diagnosis, specific to theartificial intelligence (AI) domain, which has led to the resurgence of AI in the medical field. Deep learning (DL), a major branch of AI, provides bigger room for diagnosing deadly TB disease. This review is focused on the limitations of conventional TB diagnostics and a broad description of various machine learning algorithms and their applications in TB diagnosis. Furthermore, various deep learning methods integrated with other systems such as neuro-fuzzy logic, genetic algorithm, andartificial immune systems are discussed. Finally, multiple state-of-the-art tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest are summarized to view AI-assisted future aspects in TB diagnosis.  \nKeywords: tuberculosis; deep learning; neural networks; TB diagnosis  \n1. Introduction  \nTuberculosis is a complex and chronic disease caused by a widely spread microbe, Mycobacterium tuberculosis (MTB). It is a slow-growing microbe that can ride out in extracellular and intracellular conditions [1] . It can also go into the latency phase and reverts to the exponential growth phase when the host gets into an immune-compromised condition [2] . In 2019","cbCaibBZ03Us6L8w","https://ap.wps.com/l/cbCaibBZ03Us6L8w","pdf",3332332,1,24,"English","en",105,"# Introduction\n## Conventional TB diagnostics and challenges\n## Microbiology and disease progression\n# Machine Learning Algorithms for TB Diagnosis\n## Computer-aided diagnosis (CAD) approaches\n## Detection and feature extraction in medical imaging\n# Deep Learning Methods and Integrations\n## Neuro-fuzzy logic, genetic algorithms, and artificial immune systems\n# AI-Assisted Tools and Future Aspects\n## State-of-the-art systems (CAD4TB, Lunit INSIGHT, qXR, InferRead DR Chest)","[{\"question\":\"Why is timely TB diagnosis considered critical in the review?\",\"answer\":\"Timely diagnosis and treatment are described as an essential route to full recovery. TB is characterized as a major global health threat with severe outcomes when not detected early.\"},{\"question\":\"How does CAD support TB diagnosis according to the document?\",\"answer\":\"CAD tools are presented as helpful for interpreting medical imaging and assisting radiologists in TB diagnosis. They aim to build high-performance diagnostic systems for identifying TB-related findings on radiographs.\"},{\"question\":\"What deep learning integrations are discussed for TB diagnosis?\",\"answer\":\"The review discusses deep learning methods integrated with neuro-fuzzy logic, genetic algorithms, and artificial immune systems. It also summarizes state-of-the-art AI tools such as CAD4TB, Lunit INSIGHT, qXR, and InferRead DR Chest.\"}]","Evolution of Machine Learning in Tuberculosis Diagnosis - A Review of Deep Learning-Based Medical Applications | PDF",1785820174,60,{"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},"evolution-of-machine-learning-in-tuberculosis-diagnosis-a-review-of-deep-learning-based-medical-applications","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/evolution-of-machine-learning-in-tuberculosis-diagnosis-a-review-of-deep-learning-based-medical-applications/124067/",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 timely TB diagnosis considered critical in the review?","Question",{"text":75,"@type":76},"Timely diagnosis and treatment are described as an essential route to full recovery. TB is characterized as a major global health threat with severe outcomes when not detected early.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does CAD support TB diagnosis according to the document?",{"text":80,"@type":76},"CAD tools are presented as helpful for interpreting medical imaging and assisting radiologists in TB diagnosis. They aim to build high-performance diagnostic systems for identifying TB-related findings on radiographs.",{"name":82,"@type":73,"acceptedAnswer":83},"What deep learning integrations are discussed for TB diagnosis?",{"text":84,"@type":76},"The review discusses deep learning methods integrated with neuro-fuzzy logic, genetic algorithms, and artificial immune systems. 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