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
- Nikita Niteen , Juby Mathew, Securing AI: Understanding and Defending Against Adversarial Attacks in Deep Learning Systems , International Journal on Emerging Research Areas: Vol. 3 No. 2 (2023): IJERA
- K.M Gishma, K.B Annmaria , V.N Ramna Parvan , Anagha Suresh, Athira Shaji, LIP READING AND PREDICTION SYSTEM BASED ON DEEP LEARNING , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Thomas Mathew Jose, Mathew Abraham, Sebastian Biju , Samuel Michael , Minu Cherian , Canine Dermal Analyser: Harnessing Artificial Intelligence and Deep Learning to Revolutionize Canine Skin Disease Detection , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Abid Muhammad, Alan Abdul Gafar, Abin Melvin, Bibin Varghese, A Two-Stage Deep Learning Framework for Skin Lesion Detection and Classification Using ResNet18 and EfficientNet-B4 , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Elisabeth Thomas, Arjun Saji, Aswin M S, Augustine Salas, Emil Viju, A Comprehensive Review of Advancing Cattle Monitoring and Behavior Classification using Deep Learning , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
- M Manoj, A S Athira, Rishna Ramesh, Sandhra Gopi, Firoz P U, Smart Attend Insights , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Alan K George, Arpita Mary Mathew, Asin Mary Jacob, Elizabeth Antony, Shiney Thomas, Lung Cancer Subtype Classification Using Deep Learning Models , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Jincy Lukose, Anita Ann Joseph, Meenakshy BR , Nevin Siby, Rosaine P Lal , ENHANCED PNEUMONIA DETECTION IN CHEST X-RAYS USING ATTENTION AND FNMS , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
- Richa Maria Biju, Merwin Maria Antony, Mishal Rose Thankachan, Joshua John Sajit, Bini M Issac, Enhancing Image Forgery Detection with Multi-Modal Deep Learning and Statistical Methods , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
- Anakin Rajeev, Arya B, Mekha Jose, Archanamol Lalu , Bhadra J , Hand Gesture Recognition Using Deep Learning Techniques-Review , International Journal on Emerging Research Areas: Vol. 5 No. 1 (2025): IJERA
You may also start an advanced similarity search for this article.
