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The proposed approach combines optimized training using an EM algorithm and a Bayesian split learning framework to protect patent data while improving imaging depth and tissue contrast. Results show a breast cancer model accuracy of 97.5% across 100 epochs, outperforming older models that required more epochs. The manuscript also outlines the article structure and relevant prior THz literature.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/consumer-electronics-based-smart-technologies-for-enhanced-terahertz-healthcare-having-an-integration-of-split-learning-with-medical-imaging/336226/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/consumer-electronics-based-smart-technologies-for-enhanced-terahertz-healthcare-having-an-integration-of-split-learning-with-medical-imaging/336226.png","ImageObject",300,407,{"name":92,"@type":93},"Emma Wilson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":24},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How does the paper use consumer electronics for terahertz healthcare?","Question",{"text":112,"@type":113},"It combines wearable devices and IoT for smart data collection while performing THz imaging to support medical imaging and breast cancer detection.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What training approach is used in the proposed method?",{"text":117,"@type":113},"The method uses an EM algorithm for training optimization, adjusting input parameters to improve model performance.",{"name":119,"@type":110,"acceptedAnswer":120},"How does split learning protect patient privacy in medical imaging?",{"text":121,"@type":113},"It applies a Bayesian approach with split learning to keep unprocessed data decentralized and secure, enabling privacy-preserving collaboration between institutions and scientists.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},336226,1790182556,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":24,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":41},3848291630094,"https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nConsumer electronics based smart technologies for enhanced terahertz healthcare having an integration of split learning with medical imaging  \nSambit Satpathy1*, Osamah Ibrahim Khalaf2*, Dhirendra Kumar Shukla3, SameerAlgburi4 & Habib Hamam 5,6,7,8  \nThe proposed work contains three major contribution, such as smart data collection, optimized training algorithm and integrating Bayesian approach with split learning to make privacy of the patent data. By integrating consumer electronics device such as wearable devices, and the Internet of Things (IoT) taking THz image, perform EM algorithm as training, used newly proposed slit learning method the technology promises enhanced imaging depth and improved tissue contrast, thereby enabling early and accurate disease detection the breast cancer disease. In our hybrid algorithm, the breast cancer model achieves an accuracy of 97.5 percent over 100 epochs, surpassing the less accurate old models which required a higher number of epochs, such as 165.  \nKeywords Medical imaging, Terahertz technology, Consumer electronics (CE), Split learning, Smart healthcare system  \nBreast cancer for women is very common disease in this world so that many techniques and algorithms are thereto facilitating early cancer detection and diagnosis. To consider this, here it has been introduced smart consumer electronics-based data collection through IoT based system or wearable sensor. Secondly considering Terahz medical image as THz imaging is non-invasive, meaning it doesn’t require tissue samples or ionizing radiation like X-rays. This mitigates patient discomfort and eliminates the risks associated with invasive diagnostic procedures and having high clarity. For the machine learning model in training purpose EM algorithm is used, which optimized the input parameter. After training the Bayesian approach split learning model is used for early breast cancer detection with secure the patient data.  \nThe whole article is organized as follows: Section one serves as the introduction, followed by the second and third sections, which consist of prior work and the problem definition, respectively. The fourth section outlines the proposed method, while the fifth section provides an in-depth description of the split learning-based algorithm designed for breast cancer prediction. The sixth module encompasses both the presentation of results and the subsequent discussion. Lastly, the paper concludes with a final module dedicated to summarizing key insightsand findings. This is the way in which the whole article is get structured.  \nPrior literature review  \nThe examination of state-of-the-art thermal sensors, akin to those utilized in nocturnal vision cameras, has attracted significant interest. The groundbreaking advancements in semiconductor processing techniques and the development of innovative materials have unveiled new horizons in infrared (thermal) detectors1. Terahertz (THz) technology, which functions within the frequency range of 0.3 to 3 THz, harnesses thermal radiation’s  \n1CSE, Galgotias College of Engineering and Technology, Greater Noida, Uttar Pradesh, India. 2Department of Solar, Al-Nahrain Research Center for Renewable Energy, Al-Nahrain University, Jadriya, Baghdad, Iraq. 3CSE, Galgotias University, Greater Noida, Uttar Pradesh, India. 4Al-Kitab University, 36015 Kirkuk, Iraq. 5Uni de Moncton, Moncton, NB 1EA 3E9, Canada. 6Hodmas University College, Taleh Area, Mogadishu, Somalia. 7Bridges for Academic Excellence, Tunis, Centre-Ville, Tunisia. 8School of Electrical Engineering, University of Johannesburg, Johannesburg, South Africa.* email: [sambitmails@gmail.com](sambitmails@gmail.com); [usama81818@nahrainuniv.edu.iq](usama81818@nahrainuniv.edu.iq)  \n[www. nature.com/scientificreports/](www. nature.com/scientificreports/)  \npower and presents notable benefits across diverse domains2,3. In contrast to shorter wavelengths","cbCaia0BfgYs588z","https://ap.wps.com/l/cbCaia0BfgYs588z","pdf",2033310,12,"English","# Introduction\n## Prior Work Review\n## Proposed Method and Split Learning-Based Algorithm\n## Results and Discussion\n## Conclusion","[{\"question\":\"How does the paper use consumer electronics for terahertz healthcare?\",\"answer\":\"It combines wearable devices and IoT for smart data collection while performing THz imaging to support medical imaging and breast cancer detection.\"},{\"question\":\"What training approach is used in the proposed method?\",\"answer\":\"The method uses an EM algorithm for training optimization, adjusting input parameters to improve model performance.\"},{\"question\":\"How does split learning protect patient privacy in medical imaging?\",\"answer\":\"It applies a Bayesian approach with split learning to keep unprocessed data decentralized and secure, enabling privacy-preserving collaboration between institutions and scientists.\"}]","Consumer Electronics Based Smart Technologies for Enhanced Terahertz Healthcare - Having an Integration of Split Learning with Medical Imaging | PDF",1790014195]