{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:55:22Z","timestamp":1781020522499,"version":"3.54.1"},"reference-count":67,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,18]],"date-time":"2022-06-18T00:00:00Z","timestamp":1655510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research, Qassim University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Alzheimer\u2019s Disease (AD) is a health apprehension of significant proportions that is negatively impacting the ageing population globally. It is characterized by neuronal loss and the formation of structures such as neurofibrillary tangles and amyloid plaques in the early as well as later stages of the disease. Neuroimaging modalities are routinely used in clinical practice to capture brain alterations associated with AD. On the other hand, deep learning methods are routinely used to recognize patterns in underlying data distributions effectively. This work uses Convolutional Neural Network (CNN) architectures in both 2D and 3D domains to classify the initial stages of AD into AD, Mild Cognitive Impairment (MCI) and Normal Control (NC) classes using the positron emission tomography neuroimaging modality deploying data augmentation in a random zoomed in\/out scheme. We used novel concepts such as the blurring before subsampling principle and distant domain transfer learning to build 2D CNN architectures. We performed three binaries, that is, AD\/NC, AD\/MCI, MCI\/NC and one multiclass classification task AD\/NC\/MCI. The statistical comparison revealed that 3D-CNN architecture performed the best achieving an accuracy of 89.21% on AD\/NC, 71.70% on AD\/MCI, 62.25% on NC\/MCI and 59.73% on AD\/NC\/MCI classification tasks using a five-fold cross-validation hyperparameter selection approach. Data augmentation helps in achieving superior performance on the multiclass classification task. The obtained results support the application of deep learning models towards early recognition of AD.<\/jats:p>","DOI":"10.3390\/s22124609","type":"journal-article","created":{"date-parts":[[2022,6,19]],"date-time":"2022-06-19T21:19:26Z","timestamp":1655673566000},"page":"4609","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Early-Stage Alzheimer\u2019s Disease Categorization Using PET Neuroimaging Modality and Convolutional Neural Networks in the 2D and 3D Domains"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1371-7647","authenticated-orcid":false,"given":"Ahsan Bin","family":"Tufail","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"},{"name":"Department of Electrical and Computer Engineering, COMSATS University Islamabad, Sahiwal Campus, Sahiwal 57000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nazish","family":"Anwar","sequence":"additional","affiliation":[{"name":"Registered Medical Practitioner, Pakistan Medical Commission, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0990-5805","authenticated-orcid":false,"given":"Mohamed Tahar Ben","family":"Othman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inam","family":"Ullah","sequence":"additional","affiliation":[{"name":"College of Internet of Things (IoT) Engineering, Changzhou Campus, Hohai University (HHU), Changzhou 213022, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8917-9278","authenticated-orcid":false,"given":"Rehan Ali","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Science and Technology Bannu, Bannu 28100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong-Kui","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepak","family":"Adhikari","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5203-0621","authenticated-orcid":false,"given":"Ateeq Ur","family":"Rehman","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Government College University Lahore, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7337-7608","authenticated-orcid":false,"given":"Muhammad","family":"Shafiq","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5320-1012","authenticated-orcid":false,"given":"Habib","family":"Hamam","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Universit\u00e9 de Moncton, Moncton, NB E1A3E9, Canada"},{"name":"International Institute of Technology and Management, Libreville BP1989, Gabon"},{"name":"Spectrum of Knowledge Production & Skills Development, Sfax 3027, Tunisia"},{"name":"Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5697","DOI":"10.1038\/s41598-018-22871-z","article-title":"Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer\u2019s Disease using structural MR and FDG-PET images","volume":"8","author":"Lu","year":"2018","journal-title":"Sci. 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