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 Learning
Published
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
- Sandra Saji, Melbin Mathew, Angel Mariya S, Amrutha Mugesh, Jincy Lukose, MACHINE LEARNING FOR DETECTION AND PREDICTION OF TOMATO LEAF DISEASES , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- R Karthika, Maria Toms, S R Aadrash, P U Prabath, InsightAI: Bridging Natural Language and Data Analytics , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Betzy Babu Thoppil, Anugrah Premachandran, Annapoorna M, Ashwin Mathew Zachariah, Bala Susan Jacob, Advanced Sensor-Based Landslide and Earthquake Detection and Alert System Utilizing Machine Learning and Computer Vision Technologies , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
- Arya Raj S, R Gopika Krishnan, Drishya Das, Rohith R, Jocelyn Ann Joseph, Personality Profiling Using CV Analysis , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Kaveri S, Pooja Satheesh, Kesiya Susan John, Reubel K Wilson, Dr. Jacob John, Predictive Maintenance of Machines Using IoT and Machine Learning , International Journal on Emerging Research Areas: Vol. 4 No. 2 (2024): IJERA
- Amith Bino, Don Peter Joseph, Sreehari P, Anchal J Vattakunnel, Revolutionizing Nutritional Management Through Food Scanning And Object Detection: A New Android Application For Adults , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Jyothis Joseph, Angeetha Raju, Aparna Santhosh, Ashitha Jenish, K S Minu, Survey on Fake Profile Detection in Social Media , International Journal on Emerging Research Areas: Vol. 3 No. 1 (2023): IJERA
- Dr. Indu John, Gauri Santhosh, Jesna Susan Reji, Abdul Musawir, Glady Prince, Detection of Autism Spectrum Disorder in Toddlers using Machine Learning , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Tom Kurian, Ektha P S, Chethana Raj T, Diona Joseph, Annu Mary Abraham, Intelligent Disease Prediction in Hydroponic Systems Using Machine Learning , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
- Adithya Satheesh, Ashwin S Nair, Darren Padamittam Jacob, Athul Rajeev, Er. Maheshwary Sreenath, Intrusion Countermeasure System , International Journal on Emerging Research Areas: Vol. 4 No. 1 (2024): IJERA
You may also start an advanced similarity search for this article.