Parkinsons Disease Detection Using Spiral Drawings and MRI Images

Abstract

Parkinson’s disease is a progressive neurological disorder that affects move ment and motor control, and early detection plays a critical role in improving patient care and treatment outcomes. This project, titled “Parkinson’s Disease Detection Using Spiral, Wave and MRI Images,” presents an intelligent web-based system that utilizes deep learning techniques to automatically detect Parkinson’s disease from medical and diagnostic images. The system allows healthcare staff to upload spiral drawings, wave drawings, and MRI images of patients, which are analyzed using pre-trained convolutional neural network models to classify the condition as either healthy or Parkinson-prone. The application is developed using the Django web framework for backend processing, SQLite for secure data storage, and Tensor Flow/Keras for implementing the machine learning prediction models. The system supports multiple user roles, including Admin, Staff, Doctor, and Patient, enabling efficient patient record management, automated result generation, secure access to reports, and doctor–patient interaction. By integrating artificial intelligence with a web-based healthcare platform, the proposed system enhances diagnostic accuracy, reduces manual effort, and provides a reliable and efficient solution for early detec tion and monitoring of Parkinson’s disease.

Introduction

This paper presents a deep learning-based web application for Parkinson’s Disease (PD) detection using multiple medical inputs, including spiral drawings, wave patterns, and MRI images. Parkinson’s Disease is a progressive neurological disorder caused by the degeneration of dopamine-producing neurons, leading to symptoms such as tremors, rigidity, slow movement, and balance problems. Early diagnosis is critical for effective treatment, but traditional diagnostic methods rely heavily on clinical observation and expert judgment, which can be subjective and may fail to detect the disease in its early stages.

To address these limitations, the proposed system employs artificial intelligence and deep learning, specifically Transfer Learning with the ResNet50V2 architecture, to automatically analyze medical images and classify patients as either Healthy or Parkinson Prone. The system adopts a multi-modal approach, combining motor pattern analysis from spiral and wave drawings with brain MRI imaging to improve diagnostic reliability and accuracy. Image preprocessing techniques such as resizing, normalization, and data augmentation are applied to enhance model performance and reduce overfitting.

The solution is implemented as a Django-based web application that supports multiple user roles, including Admin, Staff, Doctor, and Patient. The platform allows secure image uploading, automated disease prediction, centralized storage of results, and doctor–patient communication. Staff members manage patient registrations and image uploads, doctors review predictions and provide medical guidance, while patients can access reports and interact with healthcare professionals. Role-based authentication and database security mechanisms ensure the confidentiality and integrity of medical data.

The machine learning module serves as the core component of the system. Uploaded images are preprocessed and passed through the pre-trained ResNet50V2 model, which extracts important features and performs classification. The system stores prediction results in a relational database and displays them through interactive dashboards. SQLite is used during development, while MySQL is recommended for large-scale deployment.

Experimental evaluation demonstrated strong performance, with the model achieving approximately 97.35% training accuracy and 100% validation accuracy, indicating excellent classification capability and generalization on unseen data. The web application successfully provides real-time prediction results and supports doctor–patient interaction through an integrated query-response system.

Conclusion

In this work, a deep learning-based system for the early detection of Parkinson’s Disease has been successfully developed and implemented. The proposed system utilizes a multi-modal approach by combining spiral drawings, wave patterns, and MRI images to analyze both motor impairments and structural brain abnormalities. The use of Transfer Learning with the ResNet50V2 architecture enables efficient feature extraction and accurate classification of input images into Healthy and Parkinson Prone categories. The integration of the trained model provides a user-friendly platform that supports multiple user roles, including admin, staff, doctor, and patient. The system allows secure data entry, real-time prediction, storage of medical records, and communication between patients and doctors, making it practical for real-world healthcare applications. Experimental results demonstrate that the system achieves high accuracy, approximately 97%, along with balanced precision, recall, and F1-score values. This indicates that the proposed model is reliable and effective in detecting Parkinson’s Disease at an early stage. The multi-modal approach further enhances the robustness of the system compared to traditional single-input methods. Overall, the proposed system offers a scalable, efficient, and accessible solution for Parkinson’s Disease detection. It can assist healthcare professionals in improving diagnostic accuracy, reducing manual effort, and enabling timely intervention, thereby contributing to better patient care and management.

References

[1] Mrs. S. SasiRekha, Dr. R. Shankar, and Dr. S. Duraisamy, “Parkinsons Dis ease Prediction With Spiral Drawings and Wave Frequency Using Deep Conformal Neural Networks,” Journal of Neonatal Surgery, vol. 14, issue 4s, pp. 11–22, 2025. [2] V. V. Waykule, Siddhi Magdum, Abhishek Mule, Prachi Patil, and Mayank Ughade, “Detection of Parkinson’s Disease Using MRI and Spiral Images,” International Journal for Research in Applied Science and Engineering Technology, vol. 12, issue XI, November 2024, pp. 888-894. [3] Theyazn H. H. Aldhyani, Abdullah H. Al-Nefaie, and Deepika Koundal, “Modeling and Diagnosis Parkinson Disease by Using Hand Drawing: Deep Learning Model,” AIMS Mathematics, vol. 9, no. 3, pp. 6850–6877, 2024. DOI: 10.3934/math.2024334. [4] Megha Kamble, Prashant Shrivastava, and Megha Jain, “Digitized Spiral Drawing Classification for Parkinson’s Disease Diagnosis,” Measurement: Sensors, vol. 16, pp. 100047, 2021. DOI: 10.1016/j.measen.2021.100047. [5] Priyal Agarwal, Vipin Talreja, Rutuja Patil, Vaishnavi Jadhav, and Indu Dokare, “Early Detection of Parkinson’s Disease Using Spiral Test,” in Data Science and Big Data Analytics, Springer, pp. 391–402, 2024. DOI: 10.1007/978-981-99-9179-2-30. [6] Nesren Farhah, “Utilizing Deep Learning Models in an Intelligent Spiral Drawing Classification System for Parkinson’s Disease Classification,” Frontiers in Medicine, vol. 11, pp. 1453743, 2024. DOI: 10.3389/fmed.2024.1453743. [7] Iman Beheshti and Ji Hyun Ko, “Predicting the Occurrence of Mild Cognitive Impairment in Parkinson’s Disease Using Structural MRI Data,” Frontiers in Neuroscience, vol. 18, pp. 1375395, 2024. DOI: 10.3389/fnins.2024.1375395. [8] Junyan Fu, Hongyi Chen, Chengling Xu, Zhongzheng Jia, Qingqing Lu, Haiyan Zhang, Yue Hu, Kun Lv, Jun Zhang, and Daoying Geng, “Harnessing Routine MRI for the Early Screening of Parkinson’s Disease: A Multicenter Machine Learning Study Using T2-Weighted FLAIR Imaging,” Insights into Imaging, vol. 16, no. 92, 2025. DOI: 10.1186/s13244-025-019613. [9] Nair Ul Islam, Ruqaiya Khanam, and Ashok Kumar, “Using 3D CNN for Classification of Parkinson’s Disease from Resting-State fMRI Data,” Journal of Engineering and Applied Science, vol. 70, no. 89, 2023. DOI: 10.1186/s44147-023-00236-2. [10] Hui Wen Loh, Wanrong Hong, Chui Ping Ooi, Subrata Chakraborty, Prabal Datta Barua, Ravinesh C. Deo, Jeffrey Soar, Elizabeth E. Palmer, and U. Rajendra Acharya, “Application of Deep Learning Models for Automated Identification of Parkinson’s Disease: A Review (2011–2021),” Sensors, vol. 21, no. 21, pp. 7034, 2021. DOI: 10.3390/s21217034. [11] Yingcong Huang, Kunal Chaturvedi, Al-Akhir Nayan, Mohammad Hesam Hesamian, Ali Braytee, and Mukesh Prasad, “Early Parkinson’s Disease Diagnosis through Hand-Drawn Spiral and Wave Analysis Using Deep Learning Techniques,” Information, vol. 15, no. 4, 220, 2024. DOI: 10.3390/info15040220. [12] Venkatesan Rajinikanth, Sahar Yassine, and Syed Ahmad Chan Bukhari, “Hand-Sketches based Parkinson’s disease Screening using Lightweight Deep-Learning with Two-Fold Training and Fused Optimal Features,” International Journal of Mathematics, Statistics, and Computer Science, vol. 2, 2024. DOI: 10.59543/ijmscs.v2i.7821. [13] Nanziba Basnin, Nazmun Nahar, Fahmida Ahmed Anika, Mohammad Sha hadat Hossain, and Karl Andersson, “Deep Learning Approach to Classify Parkinson’s Disease from MRI Samples,” Lecture Notes in Computer Science, pp. 536–547, 2021. DOI: 10.1007/978-3-030-86993-9-48. [14] Ramya R., Ramesh C., Murugesan P., Nithya N., and Sathish Kumar G., “Enhancing Parkinson’s Disease Prediction Using Deep Learning-Based Convolutional Neural Networks,” J. Electrical Systems, vol. 20-5s, pp. 1866 1874, 2024. [15] Omar Alniemi and Hanaa F. Mahmood, “Convolutional neural network for the detection of Parkinson disease based on hand-drawn spiral images,” In -donesian Journal of Electrical Engineering and Computer Science, vol. 30, no. 1, pp. 267-275, April 2023. DOI: 10.11591/ijeecs.v30.i1.pp267-275. [16] Manuel Gil-Mart´ ?n, Juan Manuel Montero, and Rub´en San-Segundo, “Parkinson’s Disease Detection from Drawing Movements Using Convolutional Neural Networks,” Electronics, vol. 8, no. 8, 907, 2019. DOI: 10.3390/electronics8080907. [17] Sabyasachi Chakraborty, Satyabrata Aich, Jong-Seong-Sim, Eunyoung Han, Jinse Park, and Hee-Cheol Kim, “Parkinson’s Disease Detection from Spi- ral and Wave Drawings using Convolutional Neural Networks: A Multi stage Classifier Approach,” in Proceedings of the 22nd International Conference on Advanced Communications Technology (ICACT), pp. 298–303, 2020. DOI: 10.23919/ICACT48636.2020.9061497. [18] Mohamad Alissa, Michael A. Lones, Jeremy Cosgrove, Jane E. Alty, Stu art Jamieson, Stephen L. Smith, and Marta Vallejo, “Parkinson’s disease diagnosis using convolutional neural networks and figure-copying tasks,” Neural Computing and Applications, vol. 34, pp. 1433–1453, 2022. DOI: 10.1007/s00521-021-06469-7. [19] Meenakshi Malik, Edeh Michael Onyema, Mueen Uddin, Poonam Yadav, Aanchal Sharma, Jazlyn Jose, Achyut Shankar, Fahad Alasim, and Mustufa Haider Abidi, “Deep learning approach for Parkinson’s screening with geometric features from spiral and wave drawings,” Multimedia Tools and Applications, advance online publication, 2025. DOI: 10.1007/s11042-02520915-x. [20] A. Anisha, Femima Shelly A. T, Benitta R. K, and Amala Selciya T. L., “Parkinson’s Disease Detection using Spiral Drawings,” International Journal of Innovative Science and Research Technology, vol. 8, issue 5, May–2023: 2658-2663, 2023.

Copyright

Copyright © 2026 Aiswarya K B, Akhila A S, Arya Anish, Nahas V N, Mrs. Krishna K B. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.