Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network
<div><p>Alzheimer's Disease (AD) is a progressive, neurodegenerative brain disease and is an incurable ailment. No drug exists for AD, but its progression can be delayed if the disorder is identified at its initial stage. Therefore, an early analysis of AD is of fundamental importan...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2022
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| _version_ | 1864513519060254720 |
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| author | Ahila A (18394806) |
| author2 | Poongodi M (18394809) Mounir Hamdi (14150652) Sami Bourouis (18394812) Kulhanek Rastislav (18418854) Faizaan Mohmed (18418857) |
| author2_role | author author author author author |
| author_facet | Ahila A (18394806) Poongodi M (18394809) Mounir Hamdi (14150652) Sami Bourouis (18394812) Kulhanek Rastislav (18418854) Faizaan Mohmed (18418857) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ahila A (18394806) Poongodi M (18394809) Mounir Hamdi (14150652) Sami Bourouis (18394812) Kulhanek Rastislav (18418854) Faizaan Mohmed (18418857) |
| dc.date.none.fl_str_mv | 2022-02-07T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3389/fpubh.2022.834032 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Evaluation_of_Neuro_Images_for_the_Diagnosis_of_Alzheimer_s_Disease_Using_Deep_Learning_Neural_Network/25659108 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Public health Alzheimer’s disease accuracy convolutional neural network deep learning feature extraction image analysis image classification and positron emission tomography |
| dc.title.none.fl_str_mv | Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Alzheimer's Disease (AD) is a progressive, neurodegenerative brain disease and is an incurable ailment. No drug exists for AD, but its progression can be delayed if the disorder is identified at its initial stage. Therefore, an early analysis of AD is of fundamental importance for patient care and efficient treatment. Neuroimaging techniques aim to assist the physician in the diagnosis of brain disorders by using images. Positron emission tomography (PET) is a kind of neuroimaging technique employed to create 3D images of the brain. Due to many PET images, researchers attempted to develop computer-aided diagnosis (CAD) to differentiate normal control from AD. Most of the earlier methods used image processing techniques for preprocessing and attributes extraction and then developed a model or classifier to classify the brain images. As a result, the retrieved features had a significant impact on the recognition rate of previous techniques. A novel and enhanced CAD system based on a convolutional neural network (CNN) is formulated to address this issue, capable of discriminating normal control from Alzheimer's disease patients. The proposed approach is evaluated using the 18FDG-PET images of 855 patients, including 635 normal control and 220 Alzheimer's disease patients from the ADNI database. The result showed that the proposed CAD system yields an accuracy of 96%, a sensitivity of 96%, and a specificity of 94%, leading to splendid performance when related to the methods already in use that are specified in the literature.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Frontiers in Public Health<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fpubh.2022.834032" target="_blank">https://dx.doi.org/10.3389/fpubh.2022.834032</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_758e044a792db4adce644752ddd5cb46 |
| identifier_str_mv | 10.3389/fpubh.2022.834032 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25659108 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural NetworkAhila A (18394806)Poongodi M (18394809)Mounir Hamdi (14150652)Sami Bourouis (18394812)Kulhanek Rastislav (18418854)Faizaan Mohmed (18418857)Health sciencesPublic healthAlzheimer’s diseaseaccuracyconvolutional neural networkdeep learningfeature extractionimage analysisimage classification and positron emission tomography<div><p>Alzheimer's Disease (AD) is a progressive, neurodegenerative brain disease and is an incurable ailment. No drug exists for AD, but its progression can be delayed if the disorder is identified at its initial stage. Therefore, an early analysis of AD is of fundamental importance for patient care and efficient treatment. Neuroimaging techniques aim to assist the physician in the diagnosis of brain disorders by using images. Positron emission tomography (PET) is a kind of neuroimaging technique employed to create 3D images of the brain. Due to many PET images, researchers attempted to develop computer-aided diagnosis (CAD) to differentiate normal control from AD. Most of the earlier methods used image processing techniques for preprocessing and attributes extraction and then developed a model or classifier to classify the brain images. As a result, the retrieved features had a significant impact on the recognition rate of previous techniques. A novel and enhanced CAD system based on a convolutional neural network (CNN) is formulated to address this issue, capable of discriminating normal control from Alzheimer's disease patients. The proposed approach is evaluated using the 18FDG-PET images of 855 patients, including 635 normal control and 220 Alzheimer's disease patients from the ADNI database. The result showed that the proposed CAD system yields an accuracy of 96%, a sensitivity of 96%, and a specificity of 94%, leading to splendid performance when related to the methods already in use that are specified in the literature.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Frontiers in Public Health<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3389/fpubh.2022.834032" target="_blank">https://dx.doi.org/10.3389/fpubh.2022.834032</a></p>2022-02-07T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3389/fpubh.2022.834032https://figshare.com/articles/journal_contribution/Evaluation_of_Neuro_Images_for_the_Diagnosis_of_Alzheimer_s_Disease_Using_Deep_Learning_Neural_Network/25659108CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256591082022-02-07T03:00:00Z |
| spellingShingle | Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network Ahila A (18394806) Health sciences Public health Alzheimer’s disease accuracy convolutional neural network deep learning feature extraction image analysis image classification and positron emission tomography |
| status_str | publishedVersion |
| title | Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network |
| title_full | Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network |
| title_fullStr | Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network |
| title_full_unstemmed | Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network |
| title_short | Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network |
| title_sort | Evaluation of Neuro Images for the Diagnosis of Alzheimer's Disease Using Deep Learning Neural Network |
| topic | Health sciences Public health Alzheimer’s disease accuracy convolutional neural network deep learning feature extraction image analysis image classification and positron emission tomography |