Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography (PET) and synthetic data augmentation
With the evolution of deep learning technologies, computer vision‐related tasks achieved tremendous success in the biomedical domain. For supervised deep learning training, we need a large number of labeled datasets. The task of achieving a large number of label dataset is a challenging. The availab...
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Veröffentlicht in: | Microscopy research and technique 2021-12, Vol.84 (12), p.3023-3034 |
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description | With the evolution of deep learning technologies, computer vision‐related tasks achieved tremendous success in the biomedical domain. For supervised deep learning training, we need a large number of labeled datasets. The task of achieving a large number of label dataset is a challenging. The availability of data makes it difficult to achieve and enhance an automated disease diagnosis model's performance. To synthesize data and improve the disease diagnosis model's accuracy, we proposed a novel approach for the generation of images for three different stages of Alzheimer's disease using deep convolutional generative adversarial networks. The proposed model out‐perform in synthesis of brain positron emission tomography images for all three stages of Alzheimer disease. The three‐stage of Alzheimer's disease is normal control, mild cognitive impairment, and Alzheimer's disease. The model performance is measured using a classification model that achieved an accuracy of 72% against synthetic images. We also experimented with quantitative measures, that is, peak signal‐to‐noise (PSNR) and structural similarity index measure (SSIM). We achieved average PSNR score values of 82 for AD, 72 for CN, and 73 for MCI and SSIM average score values of 25.6 for AD, 22.6 for CN, and 22.8 for MCI.
Proposed approach detects three different stages of Alzheimer's disease using deep convolutional generative adversarial networks. Proposed model out‐performs in synthesis of brain PET images for all Alzheimer disease stages on ADNI datasetx. |
doi_str_mv | 10.1002/jemt.23861 |
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Proposed approach detects three different stages of Alzheimer's disease using deep convolutional generative adversarial networks. Proposed model out‐performs in synthesis of brain PET images for all Alzheimer disease stages on ADNI datasetx.</description><identifier>ISSN: 1059-910X</identifier><identifier>EISSN: 1097-0029</identifier><identifier>DOI: 10.1002/jemt.23861</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Alzheimer's disease ; Classification ; Cognitive ability ; Computer vision ; Data augmentation ; Datasets ; deep convolutional generative adversarial networks ; Deep learning ; Diagnosis ; Disease control ; Emissions ; Generative adversarial networks ; healthcare ; Image classification ; Machine learning ; Medical diagnosis ; medical image classification ; Medical imaging ; Model accuracy ; Neurodegenerative diseases ; Positron emission ; Positron emission tomography ; positron emission tomography (PET) scans ; public health ; Synthetic data ; synthetic image generation ; Tomography</subject><ispartof>Microscopy research and technique, 2021-12, Vol.84 (12), p.3023-3034</ispartof><rights>2021 Wiley Periodicals LLC.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3341-c60e5cb17fc77e7de454922d0a5305bf9b445d45e10d41332c7aa0d22b85803c3</citedby><cites>FETCH-LOGICAL-c3341-c60e5cb17fc77e7de454922d0a5305bf9b445d45e10d41332c7aa0d22b85803c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjemt.23861$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjemt.23861$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Sajjad, Muhammad</creatorcontrib><creatorcontrib>Ramzan, Farheen</creatorcontrib><creatorcontrib>Khan, Muhammad Usman Ghani</creatorcontrib><creatorcontrib>Rehman, Amjad</creatorcontrib><creatorcontrib>Kolivand, Mahyar</creatorcontrib><creatorcontrib>Fati, Suliman Mohamed</creatorcontrib><creatorcontrib>Bahaj, Saeed Ali</creatorcontrib><title>Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography (PET) and synthetic data augmentation</title><title>Microscopy research and technique</title><description>With the evolution of deep learning technologies, computer vision‐related tasks achieved tremendous success in the biomedical domain. For supervised deep learning training, we need a large number of labeled datasets. The task of achieving a large number of label dataset is a challenging. The availability of data makes it difficult to achieve and enhance an automated disease diagnosis model's performance. To synthesize data and improve the disease diagnosis model's accuracy, we proposed a novel approach for the generation of images for three different stages of Alzheimer's disease using deep convolutional generative adversarial networks. The proposed model out‐perform in synthesis of brain positron emission tomography images for all three stages of Alzheimer disease. The three‐stage of Alzheimer's disease is normal control, mild cognitive impairment, and Alzheimer's disease. The model performance is measured using a classification model that achieved an accuracy of 72% against synthetic images. We also experimented with quantitative measures, that is, peak signal‐to‐noise (PSNR) and structural similarity index measure (SSIM). We achieved average PSNR score values of 82 for AD, 72 for CN, and 73 for MCI and SSIM average score values of 25.6 for AD, 22.6 for CN, and 22.8 for MCI.
Proposed approach detects three different stages of Alzheimer's disease using deep convolutional generative adversarial networks. Proposed model out‐performs in synthesis of brain PET images for all Alzheimer disease stages on ADNI datasetx.</description><subject>Alzheimer's disease</subject><subject>Classification</subject><subject>Cognitive ability</subject><subject>Computer vision</subject><subject>Data augmentation</subject><subject>Datasets</subject><subject>deep convolutional generative adversarial networks</subject><subject>Deep learning</subject><subject>Diagnosis</subject><subject>Disease control</subject><subject>Emissions</subject><subject>Generative adversarial networks</subject><subject>healthcare</subject><subject>Image classification</subject><subject>Machine learning</subject><subject>Medical diagnosis</subject><subject>medical image classification</subject><subject>Medical imaging</subject><subject>Model accuracy</subject><subject>Neurodegenerative diseases</subject><subject>Positron emission</subject><subject>Positron emission tomography</subject><subject>positron emission tomography (PET) scans</subject><subject>public health</subject><subject>Synthetic data</subject><subject>synthetic image generation</subject><subject>Tomography</subject><issn>1059-910X</issn><issn>1097-0029</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kU1v1DAQhiMEEqVw4RdY4kCplOLPZHOsylKKiuCwSNyiWXuy6yWxg-1stfyb_lOcbk8cepqvZ16N5i2Kt4xeMEr5xx0O6YKLRcWeFSeMNnWZu83zOVdN2TD662XxKsYdpYwpJk-K-0-II9He7X0_Jesd9GSDDgMku0cCZo8hQrC57TDd-fCbdD6Qy_7vFu2A4X0kxkaEiET3EKPtrIZZh0zRug0ZfbQp5BIHm6c5SX7wmwDj9kDOfixXHwg4Q-LBpS0mq4mBBASmzYAuPQi9Ll500Ed88xhPi5-fl6urL-Xt9-ubq8vbUgshWakrikqvWd3pusbaoFSy4dxQUIKqddespVRGKmTUSCYE1zUANZyvF2pBhRanxdlRdwz-z4QxtflijX0PDv0UW64U5ZWkdZXRd_-hOz-F_LqZairR1AsuMnV-pHTwMQbs2jHYAcKhZbSd3Wpnt9oHtzLMjvCd7fHwBNl-XX5bHXf-Aavsmzc</recordid><startdate>202112</startdate><enddate>202112</enddate><creator>Sajjad, Muhammad</creator><creator>Ramzan, Farheen</creator><creator>Khan, Muhammad Usman Ghani</creator><creator>Rehman, Amjad</creator><creator>Kolivand, Mahyar</creator><creator>Fati, Suliman Mohamed</creator><creator>Bahaj, Saeed Ali</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QF</scope><scope>7QO</scope><scope>7QP</scope><scope>7QQ</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7SS</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>7U7</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>K9.</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>202112</creationdate><title>Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography (PET) and synthetic data augmentation</title><author>Sajjad, Muhammad ; Ramzan, Farheen ; Khan, Muhammad Usman Ghani ; Rehman, Amjad ; Kolivand, Mahyar ; Fati, Suliman Mohamed ; Bahaj, Saeed Ali</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3341-c60e5cb17fc77e7de454922d0a5305bf9b445d45e10d41332c7aa0d22b85803c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Alzheimer's disease</topic><topic>Classification</topic><topic>Cognitive ability</topic><topic>Computer vision</topic><topic>Data augmentation</topic><topic>Datasets</topic><topic>deep convolutional generative adversarial networks</topic><topic>Deep learning</topic><topic>Diagnosis</topic><topic>Disease control</topic><topic>Emissions</topic><topic>Generative adversarial networks</topic><topic>healthcare</topic><topic>Image classification</topic><topic>Machine learning</topic><topic>Medical diagnosis</topic><topic>medical image classification</topic><topic>Medical imaging</topic><topic>Model accuracy</topic><topic>Neurodegenerative diseases</topic><topic>Positron emission</topic><topic>Positron emission tomography</topic><topic>positron emission tomography (PET) scans</topic><topic>public health</topic><topic>Synthetic data</topic><topic>synthetic image generation</topic><topic>Tomography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sajjad, Muhammad</creatorcontrib><creatorcontrib>Ramzan, Farheen</creatorcontrib><creatorcontrib>Khan, Muhammad Usman Ghani</creatorcontrib><creatorcontrib>Rehman, Amjad</creatorcontrib><creatorcontrib>Kolivand, Mahyar</creatorcontrib><creatorcontrib>Fati, Suliman Mohamed</creatorcontrib><creatorcontrib>Bahaj, Saeed Ali</creatorcontrib><collection>CrossRef</collection><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Toxicology Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Microscopy research and technique</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sajjad, Muhammad</au><au>Ramzan, Farheen</au><au>Khan, Muhammad Usman Ghani</au><au>Rehman, Amjad</au><au>Kolivand, Mahyar</au><au>Fati, Suliman Mohamed</au><au>Bahaj, Saeed Ali</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography (PET) and synthetic data augmentation</atitle><jtitle>Microscopy research and technique</jtitle><date>2021-12</date><risdate>2021</risdate><volume>84</volume><issue>12</issue><spage>3023</spage><epage>3034</epage><pages>3023-3034</pages><issn>1059-910X</issn><eissn>1097-0029</eissn><abstract>With the evolution of deep learning technologies, computer vision‐related tasks achieved tremendous success in the biomedical domain. For supervised deep learning training, we need a large number of labeled datasets. The task of achieving a large number of label dataset is a challenging. The availability of data makes it difficult to achieve and enhance an automated disease diagnosis model's performance. To synthesize data and improve the disease diagnosis model's accuracy, we proposed a novel approach for the generation of images for three different stages of Alzheimer's disease using deep convolutional generative adversarial networks. The proposed model out‐perform in synthesis of brain positron emission tomography images for all three stages of Alzheimer disease. The three‐stage of Alzheimer's disease is normal control, mild cognitive impairment, and Alzheimer's disease. The model performance is measured using a classification model that achieved an accuracy of 72% against synthetic images. We also experimented with quantitative measures, that is, peak signal‐to‐noise (PSNR) and structural similarity index measure (SSIM). We achieved average PSNR score values of 82 for AD, 72 for CN, and 73 for MCI and SSIM average score values of 25.6 for AD, 22.6 for CN, and 22.8 for MCI.
Proposed approach detects three different stages of Alzheimer's disease using deep convolutional generative adversarial networks. Proposed model out‐performs in synthesis of brain PET images for all Alzheimer disease stages on ADNI datasetx.</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><doi>10.1002/jemt.23861</doi><tpages>12</tpages></addata></record> |
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subjects | Alzheimer's disease Classification Cognitive ability Computer vision Data augmentation Datasets deep convolutional generative adversarial networks Deep learning Diagnosis Disease control Emissions Generative adversarial networks healthcare Image classification Machine learning Medical diagnosis medical image classification Medical imaging Model accuracy Neurodegenerative diseases Positron emission Positron emission tomography positron emission tomography (PET) scans public health Synthetic data synthetic image generation Tomography |
title | Deep convolutional generative adversarial network for Alzheimer's disease classification using positron emission tomography (PET) and synthetic data augmentation |
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