Detection of Alzheimer’s Disease using Deep Learning

Simple item page
dc.contributor.advisorDr. K.M.A. Salam
dc.contributor.authorMd. Saron Ahmed
dc.contributor.authorSharmin Akter Mukti
dc.contributor.authorSalman Sad Shakil
dc.contributor.authorChayan Banik
dc.contributor.id1821641642
dc.contributor.id1831029642
dc.contributor.id1711064642
dc.contributor.id1521170642
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025
dc.date.accessioned2025-07-21T06:04:14Z
dc.date.available2025-07-21T06:04:14Z
dc.date.issued2023
dc.description.abstractOver the past many years, Machine Learning has been at its peak. It has many uses, such as predictive online browsing, email and text classification, detecting objects, and recognizing faces. Deep learning has grown in prominence during the past several years relative to all other machine learning applications. It helps researchers solve problems in the field of biomedicine, such as finding cancer, Alzheimer's, and malaria and figuring out what kind of blood cell something is. Deep learning is a subset of machine learning techniques used to pull out features for classification, image processing, etc. Using Magnetic Resonance Imaging (MRI) data, we classified Alzheimer patients from healthy patients using a Convolutional Neural Network (CNN). The OASIS-1 dataset has 416 people with Alzheimer's disease ranging from mild to moderate severity. Classifying this medical data is essential for making a prediction model or system that can tell if an infection is present in different people or what stage it is in. Alzheimer's disease has always been hard to put into a category, and figuring out what makes it different is the hardest part of this process. We have distinguished Alzheimer's patients from healthy participants using MRI data and various CNN architectures, including InceptionV3, Resnet50, MobileNetV2, VGG16, and VGG19, by calculating model accuracy, confusion matrix, and ROC curve. The most accurate models are the basic CNN and the InceptionV3, with an accuracy of up to 90.62 percent. This research demonstrates the performance of various CNN architectures on our MRI data of Alzheimer's patients and healthy participants in terms of classification. It enables us to identify the most effective models for detecting Alzheimer's disease.
dc.description.degreeUndergraduate
dc.identifier.cd600000196
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1297
dc.language.isoen
dc.publisherNorth South University
dc.rights©NSU Library
dc.titleDetection of Alzheimer’s Disease using Deep Learning
oaire.citation.endPage91
oaire.citation.startPage1
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
600000196.Abstract.pdf
Size:
272.97 KB
Format:
Adobe Portable Document Format
Description:
Download
Thumbnail Image
Name:
600000196.pdf
Size:
2.92 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.93 KB
Format:
Item-specific license agreed to upon submission
Description:
Download
Collections
Theses - Undergraduate