DETECTION OF ALZHEIMER’S DISEASE AND ASSISTANCE
Abstract
As the world is experiencing population growth, the portion of older people aged 65 and above is also growing. As a result, dementia with Alzheimer’s disease is expected to increase rapidly in the next few years. Currently, healthcare systems require accurate detection of the disease for its treatment and prevention. Therefore, it has become essential to develop a framework for the detection of Alzheimer’s disease to avoid complications. To this end, a novel framework based on deep learning (DL) methods is proposed to detect Alzheimer’s disease. The raw data from MRI scans are pre-processed, before applying a deep learning approach. Another feature of our system is to assist Alzheimer’s patients and their caregivers to provide support, like guidelines on how to handle them when they go through psychological depression, anger issues, etc., and how a person should behave with an Alzheimer’s patient. Therefore, our
system will be very effective and usable for patients and their relatives as well as caregivers. It can also provide insight into the disease, different stages, causes, symptoms, and related matters.
Keywords:
ResNet, DenseNet, LSTM, Deep LearningPublished
Issue
Section
License
Copyright (c) 2023 International Journal on Emerging Research Areas

This work is licensed under a Creative Commons Attribution 4.0 International License.
All published work in this journal is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
How to Cite
Similar Articles
- Mekha Jose, Avin Joshy, Abishek R Paleri, Athul Mohan, Ali Jasim R M, A Review on Contribution and Influence of Artificial Intelligence in Road Safety and Optimal Routing , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
- Tebin Joseph, Pranav Thamban Nair, Sam Kattiveettil James, Mrs Tintu Alphonsa Thomas , Pest Prediction in Rice using IoT and Feed Forward Neural Network , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Sandra Raju, Dr S Sruthy, A Reliable Method for Detecting Brain Tumors in Magnetic Resonance Images Utilizing EfficientNet , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Prinu Vinod Nair, Rohit Subash Nair, Samuel Thomas Mathew S, Ansamol Varghese, Weed detection using YOLOv3 and elimination using organic weedicides with Live feed on Web App , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Joyal Joby Joseph, Michael Abraham Philips, Noel J Abraham, Steffi Maria Saji, Shiney Thomas, A Review of Parkinson Disease Detection Techniques , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Parvathy S Kurup, Pranav P Nair, Sai Kishor, Aryan S Nair, Pranav P, Face Image Synthesis , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Adams Mathew, Adithya Sanil, Akhil J Medackal, Nikhil J Medackal, Dyni Thomas, A Literature Review on IMAGE FORGERY DETECTION , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- FATHIMA P.S, ANU ROSE JOY, ANSPIN TITUS, ANSU MARIUM SHIBU, ASNA AZEEZ, INDIAN SIGN LANGUAGE RECOGNITION USING YOLOV5 , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Dr Anil A R, Amit Sankar Arun, Anandhu Anilkumar, Anandu S Sivan, Anoop Manoharan, DESIGNING OF A VOICE – BASED PROGRAMMING IDE FOR SOURCE CODE GENERATION , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- P Sathya Narayan, Safad Ismail, Developing an Empathetic Interaction Model for Elderly in Pandemics , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
You may also start an advanced similarity search for this article.