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Journals / Bitlis Eren Üniversitesi Fen Bilimleri Dergisi / 2026 / Volume: 15 Issue: 2

Deep Learning-Based Classification of Handwriting Data: A Multi-Representation Approach For Alzheimer’s Disease

Cansu Akyürek Anacur(KARADENIZ TECHNICAL UNIVERSITY)0009-0005-8608-7892
Asuman Günay Yılmaz(KARADENIZ TECHNICAL UNIVERSITY)0000-0003-3960-5085
Bekir Dizdaroğlu(KARADENIZ TECHNICAL UNIVERSITY)0000-0002-2955-1776
Pages
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Publication Date
2026-06-30
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings indicate that numerical and visual representations derived from handwriting data contain significant discriminatory information for the classification of Alzheimer's disease and that both approaches exhibit similar and comparable levels of performance.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The study of handwriting behavior is becoming an increasingly important method for assessing neurodegenerative disorders.
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Key Findings & Data:In this study, a feature set was created by recalculating 18 features (including temporal, kinematic, and geometric characteristics) defined in the literature from raw data in the DARWIN handwriting database.
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Conclusion & Impact:The findings indicate that numerical and visual representations derived from handwriting data contain significant discriminatory information for the classification of Alzheimer's disease and that both approaches exhibit similar and comparable levels of performance.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Handwriting analysis · Feature extraction · Deep learning

Abstract

The study of handwriting behavior is becoming an increasingly important method for assessing neurodegenerative disorders. In this study, a feature set was created by recalculating 18 features (including temporal, kinematic, and geometric characteristics) defined in the literature from raw data in the DARWIN handwriting database. The resulting representations was initially evaluated using a 1-Dimensional Convolutional Neural Network (1D-CNN) to investigate the information provided by handwriting patterns for the detection of Alzheimer's disease. In the second stage, this feature set was converted into a two-dimensional image format, and the visual representations were further analyzed using the 2D Convolutional Neural Network (2D-CNN) model. The 1D-CNN model developed on numerical data achieved 94.29% accuracy, while the 2D-CNN model trained on two-dimensional visual representations achieved 91.43% accuracy. The findings indicate that numerical and visual representations derived from handwriting data contain significant discriminatory information for the classification of Alzheimer's disease and that both approaches exhibit similar and comparable levels of performance.

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Cite This Article

Anacur, C. A., Yılmaz, A. G., Dizdaroğlu, B. (2026). Deep Learning-Based Classification of Handwriting Data: A Multi-Representation Approach For Alzheimer’s Disease. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, Volume: 15 Issue: 2. https://doi.org/10.17798/bitlisfen.1843864

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