Alzheimer’s disease Detection using Deep Learning

Alzheimer’s Disease Detection using Deep Learning

PROJECT ABSTRACT:

Alzheimer disease (AD) is a neurological disorder. For the AD, there is no specific treatment. Early detection of Alzheimer’s disease can help patients receive the correct care. Many studies employ statistical and machine learning techniques to diagnose AD. The human-level performance of Deep Learning algorithms has been effectively shown in different disciplines.

In the proposed methodology, the MRI data is used to identify the AD and Deep Learning technique is used to classify the present disease. The classification of Alzheimer’s disease using deep learning methods has shown promising results, and successful application in clinical settings requires a combination of high accuracy, short processing time, and generalizability to various populations.

In this study, we developed a system of Alzheimer’s disease detection using Convolutional Neural Network (CNN) architecture using magnetic resonance imaging (MRI) scans images which are trained using Kaggle dataset. The models in this study are trained on the same dataset in order to analyse their performances.

The Convolutional Neural Network (CNN) architecture gives the highest accuracy where training accuracy is 86.34% and validation accuracy is 86.45% on the test data that detects AD accurately.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED:

CNN Model Architecture.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 4 GB

SOFTWARE REQUIREMENTS:

Operating System : Windows 10 / 11.
Coding Language : Python 3.8.
Web Framework : Flask.
Frontend : HTML, CSS, JavaScript.

Python Projects

Alzheimer’s disease Detection using Deep Learning