[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126982-en":3,"doc-seo-126982-105":29,"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},126982,687207024643,"Oliver","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",7,"Healthcare","Computer-aided diagnosis system for Alzheimer’s disease using principal component analysis and machine learning","Alzheimer’s disease is a severe, progressive neurological disorder, and although it cannot be cured, earlier detection can significantly improve patient outcomes. The document develops a machine-learning–based computer-aided diagnosis approach for Alzheimer’s disease detection and classification using brain MRI and PET data from the OASIS database. The proposed pipeline combines principal component analysis for feature extraction with support vector machines and artificial neural networks as classifiers. Results indicate that the combined PCA and supervised learning scheme achieves strong accuracy and outperforms alternative approaches.","Computer-aided diagnosis system for Alzheimer’s disease using principal component analysis and machine learning  \nbased approaches  \nLilia Lazli  \nDepartment of Computer and Software Engineering, Polytechnique Montréal, University of Montreal, 2500 Chem. de Polytechnique, Montreal, Quebec H3T 1J4, Canada  \n[lilia.lazli@polymtl.ca](lilia.lazli@polymtl.ca)  \nAbstract. Alzheimer’s disease is a severe neurological brain disorder. It is not curable, but earlier detection can help improve symptoms in a great deal. The machine learning-based approaches are popular and well-motivated models for many medical image processing tasks such as computer-aided diagnosis. These techniques can vastly improve the process for accurate diagnosis of Alzheimer’s disease. In this paper, we investigate the performance of these techniques for Alzheimer’s disease detection and classification using brain MRI and PET images from the OASIS database. The proposed system takes advantage of the powerful artificial neural network and support vector machines as classifiers, as well as principal component analysis as a feature extraction technique. The results indicate that the combined scheme achieves good accuracy and offers a significant advantage over the other approaches.  \nKeywords: Alzheimer’s disease, Computer-aided diagnosis system, Principal component analysis, Artificial neural network, Support vector machines, MRI and PET OASIS images.  \n1 INTRODUCTION  \nAlzheimer’s disease (AD) is a progressive degenerative brain disorder that gradually destroys memory, reason, judgment, language, and ultimately the ability to perform even the simplest of tasks. An automated AD classification system is crucial for the early detection of disease. This computer-aided diagnosis (CAD) system can help expert clinicians to prescribe the proper treatment and preventing brain tissue damage.  \nIn recent years, researchers have developed several CAD systems. They have developed rule-based expert systems from the 1970s to 1990s and supervised models from 1990s [1] . Moreover, several approaches have been proposed in the literature aiming at providing an automatic tool that guides the clinician in the AD diagnosis process [2] . These approaches can be categorized into two types: univariate approaches including the statistical parametric mapping (SPM) and multivariate approaches such as the voxels-as-features (VAF) approach [2] . Despite the efforts of researchers, developing an automated AD classification model remains a rather challenging task. From previous research in the medical domain, it has been proved that Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scans can perform a significant role for early detection of AD [3] . For our research work, we analyze this type of data using machine learning based model for AD classification.  \nThe machine learning has shown a prominent result for organ and substructure segmentation, several diseases classification in areas of pathology, brain, breast, bone, retina, etc. But there is little existing work for AD detection using machine learning models. Therefore, an effective machine learning model is proposed, which will help physicians working on the diagnosis of AD and help them to prescribe prompt treatment for AD patients.  \nWe developed a multi-modal classification model using a principal component analysisbased approach (PCA) in combination with supervised learning methods. We used PCA for feature extraction and support vector machines (SVMs) and artificial neural network (ANN) classifiers are trained on the features extracted from the neurological images, to detect AD from MRI and PET data. We demonstrate the performance of the CAD system on the Open Access Series of Imaging Studies (OASIS) database.  \nThe rest ofthe paper is organized as follows: Section 2 presents our proposed CAD system which combines the advantages of both PCA, and machine learning based supervised classifiers. Section 3 presents an","cbCaiaeFoW7U7Fbe","https://ap.wps.com/l/cbCaiaeFoW7U7Fbe","pdf",868435,1,"English","en",105,"# Introduction\n# Method\n## Principal component analysis","[{\"question\":\"What problem does the proposed system address?\",\"answer\":\"It addresses early detection and classification of Alzheimer’s disease by automatically predicting whether an input subject is an AD patient or healthy case.\"},{\"question\":\"How does the system extract features from MRI and PET data?\",\"answer\":\"It uses principal component analysis (PCA) after preprocessing to transform selected gray-scale characteristics into PCA-derived feature representations.\"},{\"question\":\"Which classifiers are used in the proposed CAD pipeline?\",\"answer\":\"The pipeline trains supervised classifiers, specifically support vector machines (SVMs) and artificial neural networks (ANNs), on the PCA features extracted from the images.\"}]","Computer-aided diagnosis system for Alzheimer’s disease using 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problem does the proposed system address?","Question",{"text":75,"@type":76},"It addresses early detection and classification of Alzheimer’s disease by automatically predicting whether an input subject is an AD patient or healthy case.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the system extract features from MRI and PET data?",{"text":80,"@type":76},"It uses principal component analysis (PCA) after preprocessing to transform selected gray-scale characteristics into PCA-derived feature representations.",{"name":82,"@type":73,"acceptedAnswer":83},"Which classifiers are used in the proposed CAD pipeline?",{"text":84,"@type":76},"The pipeline trains supervised classifiers, specifically support vector machines (SVMs) and artificial neural networks (ANNs), on the PCA features extracted from the 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