[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116967-en":3,"doc-seo-116967-105":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},116967,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",7,"Healthcare","Multiple Disease Detection using Machine Learning Techniques","COVID-19 has triggered global testing demands, and RT-PCR—while highly specific—can suffer from limited sensitivity and performance variations due to sampling methods and symptom onset timing. To improve lung-disease identification from imaging, this work develops and reviews machine-learning approaches using lung CT scans and lung X-ray images to predict probable diseases and diagnoses. The survey covers standard algorithms for COVID-19 detection and recent trends including TinyML, enabling faster, low-bit real-time inference and supporting clinician decision-making.","JOE International Journal of  \nOnline and Biomedical Engineering  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJOE | eISSN: 2626-8493 | Vol. 19 No. 13 (2023) |   \n[https://doi.org/10.3991/ijoe.v19i13.40523](https://doi.org/10.3991/ijoe.v19i13.40523)  \nPAPER  \nMultiple Disease Detection using Machine Learning Techniques  \nDipanjan Acharya1,  \nK. Eashwer1, Soumya Kumar1, R. Sivakumar1,  \nP.C. Kishoreraja1(􀀍), Ramasamy Srinivasagan2  \n1Vellore Institute of  \nTechnology, Vellore, India  \n2Faculty of Computer Engineering, King Faisal University, Hofuf, Saudi Arabia [kishoreraja.pc@vit.ac.in](kishoreraja.pc@vit.ac.in)  \nABSTRACT  \nThe COVID-19 disease outbreak resulted in a worldwide pandemic. Currently, the reverse transcription-polymerase chain reaction (RT-PCR), which relies on nasopharyngeal swabs to examine the existence of the ribonucleic acid (RNA) of SARS-CoV-27, is still a popular approach to testing for the disease. Despite the high level of specificity of testing with RT-PCR, the sensitivity of the method could be relatively low, and there is significant variability in efficacy depending on different sampling methods and the time of occurrence of symptoms. It is therefore essential for us to develop a machine-learning algorithm that can analyze computerized tomography images to detect the presence of COVID-19 . Besides COVID-19, lung computerized tomography (CT) scan images can detect many other diseases, such as lung cancer, pneumonia, etc. This paper deals with the implementation of an algorithm that takes lung CT scans and lung X-ray images as input and predicts a list of probable diseases and possible diagnoses that infect the lungs. Machine learning algorithms will be able to predict disease by scanning the tiniest of regions easily missed by the human eye. This paper presents a survey of various machine learning algorithms that aid in detecting multiple diseases in lung CT scan images. Apart from the study of standard algorithms best suited for COVID-19 detection, this paper also includes recent trends. One of the major recent trends that can be incorporated into COVID-19 detection is TinyML. Tiny ML is an emerging area in machine learning algorithms that can be used to detect multiple diseases in lung CT scan images with better accuracy and in less time. This tool can aid doctors in their diagnosis and treatment of patients and help increase the efficiency of the treatment process. While understanding the features and mapping them using a hidden layer, thereis a probability of compressing the dataset, as well as the model to process and classify the low-bit images in real-time using TinyML.  \nKEYWORDS  \nconvolutional neural network (CNN), KNN, KNN classifier, VGG-16, MobileNet, InceptionNet, Alex Net, TinyML  \nAcharya, D., Eashwer, K., Kumar, S., Sivakumar, R., Kishoreraja, P.C., Srinivasagan, R. (2023) . Multiple Disease Detection Using Machine Learning Techniques. International Journal of Online and Biomedical Engineering (iJOE), 19(13), pp. 120–137. [https://doi.org/10.3991/ijoe.v19i13.40523](https://doi.org/10.3991/ijoe.v19i13.40523)[ ](https://doi.org/10.3991/ijoe.v19i13.40523)[Article submitted 2023-04-16. Revision uploaded 2023-06-11. Final acceptance 2023-07-01.](Article submitted 2023-04-16. Revision uploaded 2023-06-11. Final acceptance 2023-07-01.)  \n© 2023 by the authors of this article. Published under CC-BY.  \n120 International Journal of Online and Biomedical Engineering (iJOE) iJOE | Vol. 19 No. 13 (2023)  \nMultiple Disease Detection Using Machine Learning Techniques  \n1 INTRODUCTION  \nToday’s world is being ravished by the numerous diseases that are hindering the day-to-day lives of people. There are numerous diseases that are often mistaken due to the surface-level symptoms that people are aware of, and thus they mistake them for different diseases and are often led by this premonition, while this is the case in mostly rural ar","cbCaidsKvgaKXFrX","https://ap.wps.com/l/cbCaidsKvgaKXFrX","pdf",820257,1,18,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Related works","[{\"question\":\"Why is RT-PCR not sufficient on its own for COVID-19 testing?\",\"answer\":\"RT-PCR can have relatively low sensitivity, and its efficacy varies with different sampling methods and the time of symptom occurrence.\"},{\"question\":\"How does the paper detect multiple diseases?\",\"answer\":\"It uses machine-learning algorithms that take lung CT scan images and lung X-ray images as inputs to predict likely diseases and possible diagnoses affecting the lungs.\"},{\"question\":\"What is TinyML’s role in the proposed approach?\",\"answer\":\"TinyML is highlighted as a recent trend for COVID-19 detection, enabling better accuracy with less processing time through low-bit, real-time inference.\"}]","Multiple Disease Detection using Machine Learning Techniques | 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