A Data-Driven Quest for Early Alzheimer’s Detection

Kiran Kasar, Maya Bembde

2025

Abstract

Alzheimer’s disease is a progressive neurodegenerative condition that is a major cause of dementia globally, impacting countless individuals and their families. Detecting Alzheimer’s early is essential for effective management and treatment, as it can help slow symptom progression and enhance the quality of life for those affected. Recent advancements in medical imaging and machine learning offer promising opportunities for identifying Alzheimer’s in its early stages, enabling timely interventions. This research project was initiated with the goal of leveraging cutting-edge image detection algorithms to analyze brain scan images for early signs of Alzheimer’s disease. Employing a dataset comprising various brain scans, the methodology centered around the development and validation of a machine learning model capable of distinguishing between scans indicative of Alzheimer’s and those of healthy controls. Despite the meticulous design, the project encountered significant challenges, notably data leakage and issues related to dataset quality, which have served as valuable learning experiences. This document not only summarizes the work done and the obstacles faced but also proposes a forward-looking plan aimed at overcoming these hurdles in future endeavors.

Paper Citation

in Harvard Style

Kasar K. and Bembde M. (2025). A Data-Driven Quest for Early Alzheimer’s Detection. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 568-573. DOI: 10.5220/0013596900004664

in Bibtex Style

@conference{incoft25,
author={Kiran Kasar and Maya Bembde},
title={A Data-Driven Quest for Early Alzheimer’s Detection},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={568-573},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013596900004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - A Data-Driven Quest for Early Alzheimer’s Detection
SN - 978-989-758-763-4
AU - Kasar K.
AU - Bembde M.
PY - 2025
SP - 568
EP - 573
DO - 10.5220/0013596900004664
PB - SciTePress