Attention-Enhanced Deep Learning Model for Parkinson's Diagnosis

Sakshi Mishra*
Bhilai Institute of Technology, Durg, Chhattisgarh, India.
Periodicity:January - March'2025
DOI : https://doi.org/10.26634/jip.12.1.21789

Abstract

This study presents an AI-based system for early detection of Parkinson's disease using deep learning models Inception V3 and Xception with Attention Mechanism. The system analyzes hand-drawn spiral images, which serve as biomarkers for Parkinson's symptoms like tremors and micrographia. The proposed model extracts critical features from these images using pre-trained convolutional neural networks (CNNs) enhanced with attention layers, ensuring effective classification. The dataset includes spiral drawings from both healthy individuals and Parkinson's patients, allowing the model to learn distinguishing features. The Inception V3 model achieved 100% accuracy, while the Xception model attained 88% accuracy in Parkinson's detection. To evaluate the model's performance, graphs of accuracy against epochs and loss against epochs were plotted to track learning trends. A confusion matrix was generated to analyze misclassifications, and a classification report provided insights into precision, recall, and F1-score. A comparative bar chart was also used to highlight the performance difference between Inception V3 and Xception models. This AI-driven approach provides a non-invasive, cost-effective, and automated diagnostic tool, improving early diagnosis and assisting healthcare professionals in timely intervention.

Keywords

Disease Detection, Inception V3, Xception, Attention Mechanism, Data Augmentation.

How to Cite this Article?

Mishra, S. (2025). Attention-Enhanced Deep Learning Model for Parkinson's Diagnosis. i-manager’s Journal on Image Processing, 12(1), 1-12. https://doi.org/10.26634/jip.12.1.21789

References

[3]. Allebawi, M. F., Dhieb, T., Neji, M., Farhat, N., Smaoui, E., Hamdani, T. M., & Alimi, A. M. (2024). Parkinson's Disease Detection from Online Handwriting Based on Beta-Elliptical Approach And Fuzzy Perceptual Detector. IEEE Access.
[7]. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1251-1258).
[17]. Mao, A., Mohri, M., & Zhong, Y. (2023). Cross-entropy loss functions: Theoretical analysis and applications. In International conference on Machine learning (pp. 23803-23828). PMLR.
[22]. Tolosa, E., Garrido, A., Scholz, S. W., & Poewe, W. (2021). Challenges in the diagnosis of Parkinson's disease. The Lancet Neurology, 20(5), 385-397.
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