{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T02:47:22Z","timestamp":1790390842076,"version":"4.1.0"},"reference-count":44,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T00:00:00Z","timestamp":1762214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University","award":["IMSIU-DDRSP2501"],"award-info":[{"award-number":["IMSIU-DDRSP2501"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Early detection of Parkinson\u2019s disease (PD) is vital for improving patient outcomes, yet traditional diagnostic methods often depend on subjective clinical evaluations. Methods: This study proposes a novel deep learning framework for PD detection based on spiral and wave handwriting patterns from the PaHaW dataset. A comprehensive preprocessing pipeline is implemented, integrating histogram equalization and Canny edge detection. The processed spiral and wave images are evaluated independently using a fine-tuned EfficientNetV2-S architecture for binary classification. In addition to the EfficientNetV2-S experiments, a baseline Convolutional Neural Network (CNN) model is implemented separately for the spiral and wave handwriting images. The proposed model is further assessed using a 5-fold cross-validation strategy to ensure robustness and generalizability. Results: The models achieved validation accuracies of 98.68% on the spiral dataset and 98.10% on the wave dataset, with high Receiver Operating Characteristic\u2013Area Under the Curve (ROC\u2013AUC) scores, indicating robust discrimination between healthy and PD subjects. Analysis of the confusion matrix and classification results confirmed consistent sensitivity and specificity across the dataset. The 5-fold cross-validation yielded a standard deviation of \u00b10.0109. Conclusions: These results highlight the strong potential of handwriting analysis for early PD detection.<\/jats:p>","DOI":"10.3390\/diagnostics15212795","type":"journal-article","created":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T07:58:05Z","timestamp":1762329485000},"page":"2795","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Deep Learning Framework for Early Parkinson\u2019s Disease Detection: Leveraging Spiral and Wave Handwriting Tasks with EfficientNetV2-S"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1554-5543","authenticated-orcid":false,"given":"Ayesha","family":"Razaq","sequence":"first","affiliation":[{"name":"Department of Computer Science and IT, Government Sadiq College Women University, Bahawalpur 63100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9418-6667","authenticated-orcid":false,"given":"Shabana","family":"Ramzan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and IT, Government Sadiq College Women University, Bahawalpur 63100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2127-1235","authenticated-orcid":false,"given":"Sohail","family":"Jabbar","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Munwar","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Engineering and Technology, Taxila 47050, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2675-1975","authenticated-orcid":false,"given":"Muhammad Asif","family":"Habib","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9810-1285","authenticated-orcid":false,"given":"Umar","family":"Raza","sequence":"additional","affiliation":[{"name":"Department of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Razzak, I., Kamran, I., and Naz, S. 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