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Early PD detection using spiral and wave images can improve outcomes, yet prior approaches often relied on limited datasets. This MSc research develops a machine learning framework that combines Custom GAN-generated images with a larger handwritten dataset to enhance early detection. Transfer learning with ResNet50 and InceptionV3 is evaluated with and without a KNN classifier, reporting accuracy, sensitivity, specificity, F1 score, Cohen’s Kappa, and precision.",{"@graph":69,"@context":123},[70,84,106],{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/leveraging-machine-learning-and-gans-for-parkinson-disease-detection-msc-research-project-data-analytics/290533/",{"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/leveraging-machine-learning-and-gans-for-parkinson-disease-detection-msc-research-project-data-analytics/290533.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-09","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",18,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the research address for Parkinson’s disease detection?","Question",{"text":113,"@type":114},"The research targets early PD detection using spiral and wave images while addressing prior limitations such as reduced dataset sizes.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the proposed framework generate and use training data?",{"text":118,"@type":114},"It uses a handwritten PD dataset of 1632 images per class for Custom GAN image generation, then combines generated images with the original dataset for training and evaluation.",{"name":120,"@type":111,"acceptedAnswer":121},"Which models and metrics are used to evaluate performance?",{"text":122,"@type":114},"ResNet50 and InceptionV3 are tested individually and in combination with a KNN classifier. Results are presented using accuracy, sensitivity, specificity, F1 score, Cohen’s Kappa, and precision.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290533,1789641953,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","Leveraging Machine Learning and GANs for Parkinson disease detection  \nMSc Research Project  \nData Analytics  \nCalista Gonsalves  \nStudent ID: 22186077  \nSchool of Computing  \nNational College of Ireland  \nSupervisor: Paul Stynes, Musfira Jilani and Mark Cudden  \nNational College of Ireland MSc Project Submission Sheet School of Computing  \nStudent Name: Calista Clifford Gonsalves  \nStudent ID: 22186077  \nProgramme: Msc Data Analytics Year: 2023-2024  \nModule: Research Project  \nSupervisor: Paul Stynes, Musfira Jilani and Mark Cudden  \nSubmission Due  \nDate: 12-08-2024  \nProject Title: Leveraging Machine Learning and GANs for Parkinson disease detection  \nWord Count: 6904 Page Count: 22  \nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \nSignature: Calista Clifford Gonsalves  \nDate: 12-08-2024  \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n\n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It isnot sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nLeveraging Machine Learning and GANs for Parkinson disease detection  \nCalista Gonsalves  \n22186077  \nAbstract  \nParkinson's Disease (PD) is a progressive disorder that affects the nervous system and the parts of the body controlled by nerves. Early-stage detection of PD using spiral and wave images can significantly improve patient outcomes. Many previous researchers studied classification of PD using various sources, but they faced a few limitations like reduced dataset size. Developing a machine learning framework that processes a large and varied dataset, maintains computational efficiency, and achieves high accuracy in PD detection can be a challenge. This research proposes a machine learning framework to improve the early detection of PD. The proposed framework uses the combined images generated using Custom GAN (Generative Adversarial Network) and the original dataset with both hybrid (ResNet50 and InceptionV3 with KNN classifier) and standalone pretrained CNN models(ResNet50, InceptionV3) . The hybrid model consists of a pretrained CNN model along with a machine learning classifier. This framework makes use of a handwritten Parkinson’s disease dataset (original dataset) that comprises of 1632 images each of Parkinson and healthy, for Custom GAN image generation, pre-trained CNN model training and testing. Data pre-processing and transfer learning techniques are applied to two pre-trained CNN models, namely ResNet50 and InceptionV3 . Each of these models is evaluated both individually and in combination with KNN classifier. Results of these models are presented in this paper based on accuracy, sensitivity, specificity, F1 score, Cohen’s Kappa and precision. This research shows how this framework can prove useful for patients by detecting PD at an early stage.  \n1 Introduction  \nParkinson's disease is a brain disorder that causes trouble with movement and balance, making it hard for a person to control their movements and per","cbCaigsIUOQwR97g","https://ap.wps.com/l/cbCaigsIUOQwR97g","pdf",922616,22,"English","# Introduction\n## Research aim and motivation\n## Background and related limitations\n# Abstract\n# Methodology overview","[{\"question\":\"What problem does the research address for Parkinson’s disease detection?\",\"answer\":\"The research targets early PD detection using spiral and wave images while addressing prior limitations such as reduced dataset sizes.\"},{\"question\":\"How does the proposed framework generate and use training data?\",\"answer\":\"It uses a handwritten PD dataset of 1632 images per class for Custom GAN image generation, then combines generated images with the original dataset for training and evaluation.\"},{\"question\":\"Which models and metrics are used to evaluate performance?\",\"answer\":\"ResNet50 and InceptionV3 are tested individually and in combination with a KNN classifier. Results are presented using accuracy, sensitivity, specificity, F1 score, Cohen’s Kappa, and precision.\"}]","Leveraging Machine Learning and GANs for Parkinson disease detection - MSc Research Project - Data Analytics | PDF",55]