Parvez, Mohammad ZavidHussain, EmtiazKabir, AzmainKabir, FarishtaMahmud, Md. Abu HasibSinthia, Sanzida AlamAzam, S. M. Rakibul2022-04-192022-04-1920212021-10ID 18101576ID 18101697ID 18101189ID 18101219ID 18101123http://hdl.handle.net/10361/16552Cataloged from PDF version of thesis.Includes bibliographical references (pages 41-42).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.The neurodegenerative Alzheimer's Disease is the most widely recognized cause of `Dementia' and was allegedly the 7th highest cause of death globally. Nevertheless, there is still no conclusive test for distinguishing Alzheimer's disease. Our proposed model eliminates these challenges in an effective manner. The technique fits and analyzes different classes in a single setting and requires signi ficantly less previ- ous apprehension. Several handcrafted or prede ned machine learning and deep learning models have been implemented in this fi eld of study. Our proposed multi- classi cation model is primarily implemented based on the Open Access Series of Imaging Studies (OASIS) data and suggests an 18-layer architecture. We have im- plemented a unique preprocessing approach of all three anatomical planes of the MRI scans in a single sequential model, which was also evaluated afterward. The research also explores a comparative study among multiple and binary classes in terms of performance and effciency. Prede ned models such as InceptionV3 and VGG19 have also been brought to comparison to measure the model's reliability. Our multiclass setting shows an accuracy of over 80%, which is higher than most of the existing multi-classi fication models in this dataset. Moreover, the in-depth comparative study using binary classi cation shows a signi ficant accuracy of over 92%, which ensures the overall efficacy of the model.42 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Alzheimer's diseaseCNNMulti-classBinary classMRIDeep learningEarly detectionComparative analysis18-layer3D ScansOASIS-1Artificial intelligenceMachine learning.Thesis