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Many existing methods struggle with limited medical-imaging datasets and the resulting tradeoff between precision and computational efficiency. This study proposes a framework combining HyperNet-based neural architecture search (HN-NAS) with deep convolutional GANs (DCGANs). DCGAN generates high-quality synthetic MRI images for data diversity, while HN-NAS selects effective network architectures. Results indicate improved segmentation and classification with maintained efficiency, supporting real-time clinical deployment.",{"@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/deep-convolutional-gan-and-hypernet-based-neural-architecture-search-for-brain-tumor-diagnosis-detection-and-classification/360933/",{"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/deep-convolutional-gan-and-hypernet-based-neural-architecture-search-for-brain-tumor-diagnosis-detection-and-classification/360933.png","ImageObject",300,407,{"name":92,"@type":93},"Guten tag","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the proposed framework address in brain tumor diagnosis?","Question",{"text":112,"@type":113},"It targets limitations of existing approaches caused by heterogeneous and limited medical imaging datasets, which often reduce both diagnostic precision and computational efficiency.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does DCGAN contribute to the framework?",{"text":117,"@type":113},"The DCGAN model generates high-quality synthetic MRI images of brain lesions, improving dataset diversity and mitigating limited training data.",{"name":119,"@type":110,"acceptedAnswer":120},"How does HN-NAS affect model design and computational cost?",{"text":121,"@type":113},"HN-NAS identifies efficient neural network architectures for diagnosis, and the hypernetwork generates weights for multiple candidate architectures, reducing the computational cost of architecture search.","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},360933,1790218737,{"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":44,"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":145},687212321768,"https://ap-avatar.wpscdn.com/avatar/a0010bdbe886d2fe77?x-image-process=image/resize,m_fixed,w_180,h_180&k=1789897067658708522","OPEN ACCESS  \nCitation: Swathi S, Rajalakshmi M (2026) Deep convolutional GAN and hypernet-based neural architecture search for brain tumor diagnosis detection and classification. PLoS One 21(7): e0352353. [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pone.0352353  \nEditor: Subramani Neelakandan, Hanyang University-ERICA Campus, KOREA, REPUBLIC OF  \nReceived: February 26, 2026  \nAccepted: June 9, 2026  \nPublished: July 14, 2026  \nCopyright: © 2026 Swathi, Rajalakshmi. This is an open access article distributed under the terms of the Creative Commons Attribution  License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData availability statement: All relevant data are within the manuscript.  \nFunding: The author(s) received no specific funding for this work.  \nRESEARCH ARTICLE  \nDeep convolutional GAN and hypernet-based neural architecture search for brain tumor diagnosis detection and classification  \nSreerangan Swathi1*, Murugasamy Rajalakshmi2  \n1 Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai, India, 2 Department of Information Technology, Coimbatore Institute of Technology, Coimbatore, India  \n* [swathishankar67@outlook.com](swathishankar67@outlook.com)  \nAbstract  \nBrain tumors are complex and life-threatening conditions that require accurate and efficient diagnostic approaches. However, existing approaches often face limitations in precision and computational efficiency, mainly due to the heterogeneous and limited nature of medical imaging datasets. Recent advancements in deep learning, mostly Neural Architecture Search (NAS) and Generative Adversarial Networks (GANs), have show significant potential for enhancing diagnostic performance. In this study, a novel framework integrating HyperNet-based Neural Architecture Search (HN-NAS) with Deep Convolutional Generative Adversarial Networks (DCGANs) is proposed for brain tumor detection and classification. The DCGAN model is employed to generate high-quality synthetic MRI images of brain lesions, thereby developing dataset diversity and mitigating the issue of limited training data. Meanwhile, HN-NAS is utilized to efficiently recognize optimal neural network architectures for accurate tumor diagnosis. The use of a HyperNetwork allows the generation of weights for multiple candidate architectures, provocatively decreasing the computational cost of architecture search and facilitating scalable model exploration. Experimental results establish that the proposed technique developments both segmentation and classification performance while maintaining computational efficiency. The findings designate that a reliable and scalable solution for real-time clinical applications can be accomplished by combining advanced NAS methods with generative models. Overall, this study establishes that integrating data augmentation with architecture optimization can suggestively improve medical imaging diagnosis.  \nIntroduction  \nThe brain is the most developed organ in the human body, consisting of intricate neural networks. The neurons relate to one another and act as a response to other body organs. The development of brain cancer takes place when there is abnormal  \nPLOS One | [https://doi.org/10.1371/journal.pone.0352353](https://doi.org/10.1371/journal.pone.0352353) July 14, 2026 1 / 25  \nCompeting interests: The authors have declared that no competing interests exist.  \nfunctioning of these neurons. Cancer is estimated to result in about 9.6 million deaths worldwide in 2018, and brain tumors are amongst the top causes of cancer deaths. Tumors of the brain claim close to 10% of the world deaths [ 1] . Brain tumors can be categorized as benign (non-cancerous) or malignant (cancerous), as well as primary or secondary. While benign tumors are usually contained and do not infiltrate surrounding tissues, malignant tumors often develop quickl","cbCais51Rm1MEj39","https://ap.wps.com/l/cbCais51Rm1MEj39","pdf",2322715,25,"English","# Abstract\n# Introduction","[{\"question\":\"What problem does the proposed framework address in brain tumor diagnosis?\",\"answer\":\"It targets limitations of existing approaches caused by heterogeneous and limited medical imaging datasets, which often reduce both diagnostic precision and computational efficiency.\"},{\"question\":\"How does DCGAN contribute to the framework?\",\"answer\":\"The DCGAN model generates high-quality synthetic MRI images of brain lesions, improving dataset diversity and mitigating limited training data.\"},{\"question\":\"How does HN-NAS affect model design and computational cost?\",\"answer\":\"HN-NAS identifies efficient neural network architectures for diagnosis, and the hypernetwork generates weights for multiple candidate architectures, reducing the computational cost of architecture search.\"}]","Deep convolutional GAN and hypernet-based neural architecture search for brain tumor diagnosis detection and classification | PDF",1790142080,63]