Open Access
| Issue |
BIO Web Conf.
Volume 232, 2026
2026 16th International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB 2026)
|
|
|---|---|---|
| Article Number | 06002 | |
| Number of page(s) | 10 | |
| Section | AI-Driven Biomedical Text Mining and Intelligent Disease Diagnosis | |
| DOI | https://doi.org/10.1051/bioconf/202623206002 | |
| Published online | 24 April 2026 | |
- N. Yang, J. Liu, D. Sun, J. Ding, L. Sun, X. Qi, W. Yan. Motor symptoms of Parkinson's disease: Critical markers for early AI-assisted diagnosis. Front. Aging Neurosci. 17, 1602426 (2025). https://doi.org/10.3389/fnagi.2025.1602426 [Google Scholar]
- S. B. Dias, A. Grammatikopoulou, J. A. Diniz, K. Dimitropoulos, N. Grammalidis, V. Zilidou, L. J. Hadjileontiadis. Innovative Parkinson's Disease Patients' Motor Skills Assessment: the I-PROGNOSIS paradigm. Front. Comput. Sci. 2, 20 (2020). https://doi.org/10.3389/fcomp.2020.00020 [Google Scholar]
- H. Suominen, M. Manocha, J. Desborough, A. Parkinson, D. Apthorp. Finger tapping measures for Parkinson's disease: Preliminary evaluation of an Android application for data collection in Australia. Stud. Health Technol. Inform. (2021). https://doi.org/10.3233/shti210775 [Google Scholar]
- A. Zemmar, A. Bennour, M. Tahar, D. Ghabban, M. Al-Sarem. Unveiling Parkinson's: Handwriting symptoms with explainable and interpretable CNN model. Int. J. Pattern Recognit. Artif. Intell. 39 (2025). https://doi.org/10.1142/S0218001424400019 [Google Scholar]
- E. Alniemi, A. Mahmood. Convolutional neural networks for Parkinson's disease detection based on handwriting analysis. Comput. Biol. Med. 162, 107185 (2023). http://doi.org/10.11591/iieecs.v30.i1.pp267-275 [Google Scholar]
- S. K., N. T. M. Design of a deep fusion model for early Parkinson's disease prediction using handwritten image analysis. Sci. Rep. 15, (2025). https://doi.org/10.1038/s41598-025-04807-6 [Google Scholar]
- A. Althnian, D. AlSaeed, H. Al-Baity, A. Samha, A. B. Dris, N. Alzakari, H. Kurdi. Impact of dataset size on classification performance: an empirical evaluation in the medical domain. Appl. Sci. 11, 796 (2021). https://doi.org/10.3390/app11020796 [Google Scholar]
- F. Prinzi, T. Currieri, S. Gaglio, S. Vitabile. Shallow and deep learning classifiers in medical image analysis. Eur. Radiol. Exp. 8, (2024). https://doi.org/10.1186/s41747-024-00428-2 [Google Scholar]
- N. Kumar, M. Gupta, D. Gupta, S. Tiwari. Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images. J. Ambient Intell. Humaniz. Comput. 14, 469–478 (2021). https://doi.org/10.1007/s12652-021-03306-6 [Google Scholar]
- A. W. Salehi, S. Khan, G. Gupta, B. I. Alabduallah, A. Almjally, H. Alsolai, A. Mellit. A study of CNN and transfer learning in medical imaging: Advantages, challenges, future scope. Sustainability 15, 5930 (2023). https://doi.org/10.3390/su15075930 [Google Scholar]
- R. Guido, S. Ferrisi, D. Lofaro, D. Conforti. An Overview on the advancements of support vector machine models in healthcare Applications: a review. Information 15, 235 (2024). https://doi.org/10.3390/info15040235 [Google Scholar]
- M. Shanbehzadeh, H. Kazemi-Arpanahi, M. B. Ghalibaf, A. Orooji. Performance evaluation of machine learning for breast cancer diagnosis: A case study. Inform. Med. Unlocked 31, 101009 (2022). https://doi.org/10.1016/j.imu.2022.101009 [Google Scholar]
- M.A. Ganaie, M. Hu, M. Tanveer, P.N. Suganthan, Ensemble deep learning: A review, Eng. Appi. Artif. Intell. 115, 105151 (2022). https://doi.org/10.1016/j.engappai.2022.105151 [Google Scholar]
- A. Aldwgeri, N. Abubacker. Ensemble of deep convolutional neural networks for skin lesion classification in dermoscopy images. In Proc. Int. Conf. Adv. Comput. Intell. (2019). https://doi.org/10.1007/978-3-030-34032-2_20 [Google Scholar]
- A. Narin, C. Kaya, Z. Pamuk. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. Pattern Anal. Appl. 24, 1207–1220 (2021). https://doi.org/10.1007/s10044-021-00984-y [Google Scholar]
- S. Sushith, M. Sathiya, V Kalaipoonguzhali, V. Sathya. A hybrid deep learning framework for early detection of diabetic retinopathy using retinal fundus images. Sci. Rep. 15 (2025). https://doi.org/10.1038/s41598-025-99309-w [Google Scholar]
- Mukherjee, S. (2022, August 18). The annotated ReSNet-50 - TDS Archive - medium. Medium. https://medium.com/data-science/the-annotated-resnet-50-a6c536034758 [Google Scholar]
- M. Shaban. Deep Learning for Parkinson's Disease Diagnosis: A Short Survey. Computers 12, 58 (2023). https://doi.org/10.3390/computers12030058 [Google Scholar]
- Deepfake detection. J. Multidiscip. Knowl. 5, 63–74 (2025). https://doi.org/10.36676/jmk.v5.i2.76 [Google Scholar]
- Sharifrazi, D., Alizadehsani, R., Roshanzamir, M., Joloudari, J. H., Shoeibi, A., Gorriz, J. M., & Sani, Z. A. (2021). Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images. Biomedical Signal Processing and Control, 68, 102622. https://doi.org/10.1016/jbspc.2021.102622 [Google Scholar]
- Ahlawat, S., & Choudhary, A. (2020). Hybrid CNN-SVM classifier for handwritten digit recognition. Procedia Computer Science, 167, 2554–2560. https://doi.org/10.1016/j.procs.2020.03.309 [Google Scholar]
- N. M. Ranjan, G. Mate, M. Bembde. Detection of Parkinson's Disease using Machine Learning Algorithms and Handwriting Analysis. J. Data Min. Manag. 8, 21–29 (2023). https://doi.org/10.46610/jodmm.2023.v08i0L004 [Google Scholar]
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