ParkinNet: a Novel Approach to Classifying Alzheimer’s and Parkinson’s Diseases Using Brain Structural MRI
Abstract
Both Parkinson’s disease (PD) and Alzheimer’s disease (AD) are forms of neurodegeneration, which are linked to the same biochemical alterations in the brain. The mixed pathology of these diseases may cause diagnostic dilemmas, which may lead to misdiagnosis. Because of this, classification of AD and PD is essential to reduce extra healthcare costs and the patients’ stress. However, the classification of AD and PD can be challenging because of the overlapping symptoms and risk factors. Therefore, the purpose of this study is to develop a model named ParkinNet to classify AD and PD. The current study used Global Average Pooling and Adam optimiser with a batch size of 64. For evaluation, seven deep learning algorithms are used, including MobileNetV2, EfficientNetB2, InceptionResNetv2, VGG16, VGG19, InceptionV3 and ResNet50, along with the proposed ParkinNet model. The proposed ParkinNet model outperforms the other existing models examined in this study and yields an accuracy of 98.54 %. The precise classification of these diseases may contribute to the diagnosis process of AD and PD.
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Balestrino, R., & Schapira, A. (2020). Parkinson disease. European Journal of Neurology, 27(1), 27–42. https://doi.org/10.1111/ene.14108
Berahim, M., Samsudin, N. A., & Nathan, S. S. (2018). A review: Image analysis techniques to improve labeling accuracy of medical image classification. In Recent Advances on Soft Computing and Data Mining (pp. 298–307). Springer. https://doi.org/10.1007/978-3-319-76351-4_29
Chen, Z., Li, G., & Liu, J. (2020). Autonomic dysfunction in Parkinson’s disease: Implications for pathophysiology, diagnosis, and treatment. Neurobiology of Disease, 134, 104700. https://doi.org/10.1016/j.nbd.2019.104700
Choi, H., Kim, Y. K., Yoon, E. J., Lee, J.-Y., Lee, D. S., & Alzheimer’s Disease Neuroimaging Initiative. (2020). Cognitive signature of brain FDG PET based on deep learning: Domain transfer from Alzheimer’s disease to Parkinson’s disease. European Journal of Nuclear Medicine and Molecular Imaging, 47, 403–412. https://doi.org/10.1007/s00259-019-04526-3
Compta, Y., Parkkinen, L., O’Sullivan, S. S., Vandrovcova, J., Holton, J. L., Collins, C., Lashley, T., Kallis, C., Williams, D. R., de Silva, R., & Lees, A. J. (2011). Lewy- and Alzheimer-type pathologies in Parkinson’s disease dementia: Which is more important? Brain, 134(5), 1493–1505. https://doi.org/10.1093/brain/awr098
Faisal, A., Dharma, T. N. A., Ferani, W., Widi, P. G., & Rizka, S. F. (2021). Comparative study of VGG16 and MobileNetV2 for masked face recognition. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, 6(2), 230–237. https://doi.org/10.26555/jiteki.v6i2.21120
Fan, Z., Xu, F., Li, C., & Yao, L. (2020). Application of KPCA and AdaBoost algorithm in classification of functional magnetic resonance imaging of Alzheimer’s disease. Neural Computing and Applications, 32, 5329–5338. https://doi.org/10.1007/s00521-018-3523-4
Goyal, J., Khandnor, P., & Aseri, T. C. (2021). A comparative analysis of machine learning classifiers for dysphonia-based classification of Parkinson’s disease. International Journal of Data Science and Analytics, 11, 69–83. https://doi.org/10.1007/s41060-020-00224-4
Iravani, B., Towhidkhah, F., & Roghani, M. (2014). A new feature extraction method and classification of early stage parkinsonian rats with and without DBS treatment. Australasian Physical & Engineering Sciences in Medicine, 37, 655–664. https://doi.org/10.1007/s13246-014-0305-9
Jahan, S., Abu Taher, K., Kaiser, M. S., Mahmud, M., Rahman, M. S., Hosen, A. S., & Ra, I.-H. (2023). Explainable AI-based Alzheimer’s prediction and management using multimodal data. PLOS ONE, 18(11), e0294253. https://doi.org/10.1371/journal.pone.0294253
Jankovic, J. (2008). Parkinson’s disease: Clinical features and diagnosis. Journal of Neurology, Neurosurgery & Psychiatry, 79(4), 368–376. https://doi.org/10.1136/jnnp.2007.131045
Jaworek-Korjakowska, J., Kleczek, P., & Gorgon, M. (2019). Melanoma thickness prediction based on convolutional neural network with VGG-19 model transfer learning. CVPR Workshops, 0–0. https://openaccess.thecvf.com/content_CVPRW_2019/html/CVPM/Jaworek-Korjakowska_Melanoma_Thickness_Prediction_Based_on_Convolutional_Neural_Network_With_CVPRW_2019_paper.html
Kalia, L. V., & Lang, A. E. (2015). Parkinson’s disease. The Lancet, 386(9996), 896–912. https://doi.org/10.1016/S0140-6736(14)61393-3
Kaur, S., Aggarwal, H., & Rani, R. (2020). Hyper-parameter optimization of deep learning model for prediction of Parkinson’s disease. Machine Vision and Applications, 31, 1–15. https://doi.org/10.1007/s00138-020-01068-4
Kaur, S., Aggarwal, H., & Rani, R. (2021). Diagnosis of Parkinson’s disease using deep CNN with transfer learning and data augmentation. Multimedia Tools and Applications, 80(7), 10113–10139. https://doi.org/10.1007/s11042-020-10150-4
Khatri, D. K., Choudhary, M., Sood, A., & Singh, S. B. (2020). Anxiety: An ignored aspect of Parkinson’s disease lacking attention. Biomedicine & Pharmacotherapy, 131, 110776. https://doi.org/10.1016/j.biopha.2020.110776
Martinez-Eguiluz, M., Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Perona, I., Murueta-Goyena, A., Acera, M., Del Pino, R., Tijero, B., & Gomez-Esteban, J. C. (2023). Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies. Neural Computing and Applications, 35(8), 5603–5617. https://doi.org/10.1007/s00521-022-07651-3
Mittal, V., & Sharma, R. (2021). Machine learning approach for classification of Parkinson disease using acoustic features. Journal of Reliable Intelligent Environments, 7(3), 233–239. https://doi.org/10.1007/s40860-021-00134-9
Nagasubramanian, G., & Sankayya, M. (2021). Multivariate vocal data analysis for detection of Parkinson disease using deep learning. Neural Computing and Applications, 33(10), 4849–4864. https://doi.org/10.1007/s00521-020-05358-3
Naseer, A., Rani, M., Naz, S., Razzak, M. I., Imran, M., & Xu, G. (2020). Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Computing and Applications, 32(3), 839–854. https://doi.org/10.1007/s00521-018-3933-3
National Institute of Aging. (2019). What causes Alzheimer’s disease? https://www.nia.nih.gov/health/alzheimers-causes-and-risk-factors/what-causesalzheimers-disease
Noor, M. B. T., Zenia, N. Z., Kaiser, M. S., Mamun, S. A., & Mahmud, M. (2020). Application of deep learning in detecting neurological disorders from magnetic resonance images: A survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain Informatics, 7, 1–21. https://doi.org/10.1186/s40708-020-00112-2
Plascencia-Villa, G., & Perry, G. (2022). Neuropathologic changes provide insights into key mechanisms of Alzheimer disease and related dementia. The American Journal of Pathology, 192(10), 1340–1346. https://doi.org/10.1016/j.ajpath.2022.06.006
Pradhan, A. K., Mishra, D., Das, K., Obaidat, M. S., & Kumar, M. (2023). A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms. Multimedia Tools and Applications, 82(9), 14219–14237. https://doi.org/10.1007/s11042-022-13459-7
Prasuhn, J., Heldmann, M., Münte, T. F., & Brüggemann, N. (2020). A machine learning-based classification approach on Parkinson’s disease diffusion tensor imaging datasets. Neurological Research and Practice, 2, 1–5. https://doi.org/10.1186/s42466-020-00052-6
Sabbagh, M. N., Adler, C. H., Lahti, T. J., Connor, D. J., Vedders, L., Peterson, L. K., Caviness, J. N., Shill, H. A., Sue, L. I., Ziabreva, I., & Beach, T. G. (2009). Parkinson disease with dementia: Comparing patients with and without Alzheimer pathology. Alzheimer Disease & Associated Disorders, 23(3), 295–297. https://doi.org/10.1097/WAD.0b013e3181a6be30
Sadeghi Razlighi, M., Jafari, A. H., Firoozabadi, S. M., & Shahidi, G. A. (2012). Study of chaotic behavior of tremor of some Parkinsonians under deep brain stimulation. Australasian Physical & Engineering Sciences in Medicine, 35, 25–30. https://doi.org/10.1007/BF03321839
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4510–4520). https://doi.org/10.1109/CVPR.2018.00474
Sarica, A., Quattrone, A., & Quattrone, A. (2022). Explainable machine learning with pairwise interactions for the classification of Parkinson’s disease and SWEDD from clinical and imaging features. Brain Imaging and Behavior, 16(5), 2188–2198. https://doi.org/10.1007/s11682-021-00562-2
Silva, M. V. F., Loures, C. d. M. G., Alves, L. C. V., de Souza, L. C., Borges, K. B. G., & Carvalho, M. d. G. (2019). Alzheimer’s disease: Risk factors and potentially protective measures. Journal of Biomedical Science, 26, 1–11. https://doi.org/10.1186/s12929-019-0524-y
Sissons, B. (2022). What is the difference between Parkinson’s disease and Alzheimer’s disease? https://www.medicalnewstoday.com/articles/parkinsons-vs-alzheimers
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11231
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2818–2826). https://doi.org/10.1109/CVPR.2016.308
Taleb, C., Likforman-Sulem, L., Mokbel, C., & Khachab, M. (2020). Detection of Parkinson’s disease from handwriting using deep learning: A comparative study. Evolutionary Intelligence. https://doi.org/10.1007/s12065-020-00456-0
Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the International Conference on Machine Learning (pp. 6105–6114). https://proceedings.mlr.press/v97/tan19a.html
Tolosa, E., Wenning, G., & Poewe, W. (2006). The diagnosis of Parkinson’s disease. The Lancet Neurology, 5(1), 75–86. https://doi.org/10.1016/S1474-4422(05)70285-4
Tong, Q., & Chen, L. (2021). Associations of Alzheimer’s disease neuropathologic changes with clinical presentations of Parkinson’s disease. Journal of Alzheimer’s Disease, 81(1), 201–207. https://doi.org/10.3233/JAD-210408
UCSF Memory and Aging Center. (n.d.). Parkinson’s disease dementia. https://memory.ucsf.edu/dementia/parkinsons/parkinson-disease-dementia
University of Rochester Medical Center. (n.d.). Anatomy of the brain. https://www.urmc.rochester.edu/encyclopedia/content.aspx?ContentTypeID=85&ContentID=P00773
World Health Organization. (2023, March 15). Dementia. https://www.who.int/news-room/fact-sheets/detail/dementia
Xie, A., Gao, J., Xu, L., & Meng, D. (2014). Shared mechanisms of neurodegeneration in Alzheimer’s disease and Parkinson’s disease. BioMed Research International, 2014. https://doi.org/10.1155/2014/717419
Yücelbaş, C. (2021). A new approach: Information gain algorithm-based k-nearest neighbors hybrid diagnostic system for Parkinson’s disease. Physical and Engineering Sciences in Medicine, 44(2), 511–524. https://doi.org/10.1007/s13246-021-01001-4
Zegers, C., Posch, J., Traverso, A., Eekers, D., Postma, A., Backes, W., Dekker, A., & van Elmpt, W. (2021). Current applications of deep-learning in neuro-oncological MRI. Physica Medica, 83, 161–173. https://doi.org/10.1016/j.ejmp.2021.03.007
Zesiewicz, T. A., Baker, M. J., Wahba, M., & Hauser, R. A. (2003). Autonomic nervous system dysfunction in Parkinson’s disease. Current Treatment Options in Neurology, 5(2), 149–160. https://doi.org/10.1007/s11940-003-0021-6
Berahim, M., Samsudin, N. A., & Nathan, S. S. (2018). A review: Image analysis techniques to improve labeling accuracy of medical image classification. In Recent Advances on Soft Computing and Data Mining (pp. 298–307). Springer. https://doi.org/10.1007/978-3-319-76351-4_29
Chen, Z., Li, G., & Liu, J. (2020). Autonomic dysfunction in Parkinson’s disease: Implications for pathophysiology, diagnosis, and treatment. Neurobiology of Disease, 134, 104700. https://doi.org/10.1016/j.nbd.2019.104700
Choi, H., Kim, Y. K., Yoon, E. J., Lee, J.-Y., Lee, D. S., & Alzheimer’s Disease Neuroimaging Initiative. (2020). Cognitive signature of brain FDG PET based on deep learning: Domain transfer from Alzheimer’s disease to Parkinson’s disease. European Journal of Nuclear Medicine and Molecular Imaging, 47, 403–412. https://doi.org/10.1007/s00259-019-04526-3
Compta, Y., Parkkinen, L., O’Sullivan, S. S., Vandrovcova, J., Holton, J. L., Collins, C., Lashley, T., Kallis, C., Williams, D. R., de Silva, R., & Lees, A. J. (2011). Lewy- and Alzheimer-type pathologies in Parkinson’s disease dementia: Which is more important? Brain, 134(5), 1493–1505. https://doi.org/10.1093/brain/awr098
Faisal, A., Dharma, T. N. A., Ferani, W., Widi, P. G., & Rizka, S. F. (2021). Comparative study of VGG16 and MobileNetV2 for masked face recognition. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, 6(2), 230–237. https://doi.org/10.26555/jiteki.v6i2.21120
Fan, Z., Xu, F., Li, C., & Yao, L. (2020). Application of KPCA and AdaBoost algorithm in classification of functional magnetic resonance imaging of Alzheimer’s disease. Neural Computing and Applications, 32, 5329–5338. https://doi.org/10.1007/s00521-018-3523-4
Goyal, J., Khandnor, P., & Aseri, T. C. (2021). A comparative analysis of machine learning classifiers for dysphonia-based classification of Parkinson’s disease. International Journal of Data Science and Analytics, 11, 69–83. https://doi.org/10.1007/s41060-020-00224-4
Iravani, B., Towhidkhah, F., & Roghani, M. (2014). A new feature extraction method and classification of early stage parkinsonian rats with and without DBS treatment. Australasian Physical & Engineering Sciences in Medicine, 37, 655–664. https://doi.org/10.1007/s13246-014-0305-9
Jahan, S., Abu Taher, K., Kaiser, M. S., Mahmud, M., Rahman, M. S., Hosen, A. S., & Ra, I.-H. (2023). Explainable AI-based Alzheimer’s prediction and management using multimodal data. PLOS ONE, 18(11), e0294253. https://doi.org/10.1371/journal.pone.0294253
Jankovic, J. (2008). Parkinson’s disease: Clinical features and diagnosis. Journal of Neurology, Neurosurgery & Psychiatry, 79(4), 368–376. https://doi.org/10.1136/jnnp.2007.131045
Jaworek-Korjakowska, J., Kleczek, P., & Gorgon, M. (2019). Melanoma thickness prediction based on convolutional neural network with VGG-19 model transfer learning. CVPR Workshops, 0–0. https://openaccess.thecvf.com/content_CVPRW_2019/html/CVPM/Jaworek-Korjakowska_Melanoma_Thickness_Prediction_Based_on_Convolutional_Neural_Network_With_CVPRW_2019_paper.html
Kalia, L. V., & Lang, A. E. (2015). Parkinson’s disease. The Lancet, 386(9996), 896–912. https://doi.org/10.1016/S0140-6736(14)61393-3
Kaur, S., Aggarwal, H., & Rani, R. (2020). Hyper-parameter optimization of deep learning model for prediction of Parkinson’s disease. Machine Vision and Applications, 31, 1–15. https://doi.org/10.1007/s00138-020-01068-4
Kaur, S., Aggarwal, H., & Rani, R. (2021). Diagnosis of Parkinson’s disease using deep CNN with transfer learning and data augmentation. Multimedia Tools and Applications, 80(7), 10113–10139. https://doi.org/10.1007/s11042-020-10150-4
Khatri, D. K., Choudhary, M., Sood, A., & Singh, S. B. (2020). Anxiety: An ignored aspect of Parkinson’s disease lacking attention. Biomedicine & Pharmacotherapy, 131, 110776. https://doi.org/10.1016/j.biopha.2020.110776
Martinez-Eguiluz, M., Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Perona, I., Murueta-Goyena, A., Acera, M., Del Pino, R., Tijero, B., & Gomez-Esteban, J. C. (2023). Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies. Neural Computing and Applications, 35(8), 5603–5617. https://doi.org/10.1007/s00521-022-07651-3
Mittal, V., & Sharma, R. (2021). Machine learning approach for classification of Parkinson disease using acoustic features. Journal of Reliable Intelligent Environments, 7(3), 233–239. https://doi.org/10.1007/s40860-021-00134-9
Nagasubramanian, G., & Sankayya, M. (2021). Multivariate vocal data analysis for detection of Parkinson disease using deep learning. Neural Computing and Applications, 33(10), 4849–4864. https://doi.org/10.1007/s00521-020-05358-3
Naseer, A., Rani, M., Naz, S., Razzak, M. I., Imran, M., & Xu, G. (2020). Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Computing and Applications, 32(3), 839–854. https://doi.org/10.1007/s00521-018-3933-3
National Institute of Aging. (2019). What causes Alzheimer’s disease? https://www.nia.nih.gov/health/alzheimers-causes-and-risk-factors/what-causesalzheimers-disease
Noor, M. B. T., Zenia, N. Z., Kaiser, M. S., Mamun, S. A., & Mahmud, M. (2020). Application of deep learning in detecting neurological disorders from magnetic resonance images: A survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain Informatics, 7, 1–21. https://doi.org/10.1186/s40708-020-00112-2
Plascencia-Villa, G., & Perry, G. (2022). Neuropathologic changes provide insights into key mechanisms of Alzheimer disease and related dementia. The American Journal of Pathology, 192(10), 1340–1346. https://doi.org/10.1016/j.ajpath.2022.06.006
Pradhan, A. K., Mishra, D., Das, K., Obaidat, M. S., & Kumar, M. (2023). A COVID-19 X-ray image classification model based on an enhanced convolutional neural network and hill climbing algorithms. Multimedia Tools and Applications, 82(9), 14219–14237. https://doi.org/10.1007/s11042-022-13459-7
Prasuhn, J., Heldmann, M., Münte, T. F., & Brüggemann, N. (2020). A machine learning-based classification approach on Parkinson’s disease diffusion tensor imaging datasets. Neurological Research and Practice, 2, 1–5. https://doi.org/10.1186/s42466-020-00052-6
Sabbagh, M. N., Adler, C. H., Lahti, T. J., Connor, D. J., Vedders, L., Peterson, L. K., Caviness, J. N., Shill, H. A., Sue, L. I., Ziabreva, I., & Beach, T. G. (2009). Parkinson disease with dementia: Comparing patients with and without Alzheimer pathology. Alzheimer Disease & Associated Disorders, 23(3), 295–297. https://doi.org/10.1097/WAD.0b013e3181a6be30
Sadeghi Razlighi, M., Jafari, A. H., Firoozabadi, S. M., & Shahidi, G. A. (2012). Study of chaotic behavior of tremor of some Parkinsonians under deep brain stimulation. Australasian Physical & Engineering Sciences in Medicine, 35, 25–30. https://doi.org/10.1007/BF03321839
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4510–4520). https://doi.org/10.1109/CVPR.2018.00474
Sarica, A., Quattrone, A., & Quattrone, A. (2022). Explainable machine learning with pairwise interactions for the classification of Parkinson’s disease and SWEDD from clinical and imaging features. Brain Imaging and Behavior, 16(5), 2188–2198. https://doi.org/10.1007/s11682-021-00562-2
Silva, M. V. F., Loures, C. d. M. G., Alves, L. C. V., de Souza, L. C., Borges, K. B. G., & Carvalho, M. d. G. (2019). Alzheimer’s disease: Risk factors and potentially protective measures. Journal of Biomedical Science, 26, 1–11. https://doi.org/10.1186/s12929-019-0524-y
Sissons, B. (2022). What is the difference between Parkinson’s disease and Alzheimer’s disease? https://www.medicalnewstoday.com/articles/parkinsons-vs-alzheimers
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.11231
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2818–2826). https://doi.org/10.1109/CVPR.2016.308
Taleb, C., Likforman-Sulem, L., Mokbel, C., & Khachab, M. (2020). Detection of Parkinson’s disease from handwriting using deep learning: A comparative study. Evolutionary Intelligence. https://doi.org/10.1007/s12065-020-00456-0
Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the International Conference on Machine Learning (pp. 6105–6114). https://proceedings.mlr.press/v97/tan19a.html
Tolosa, E., Wenning, G., & Poewe, W. (2006). The diagnosis of Parkinson’s disease. The Lancet Neurology, 5(1), 75–86. https://doi.org/10.1016/S1474-4422(05)70285-4
Tong, Q., & Chen, L. (2021). Associations of Alzheimer’s disease neuropathologic changes with clinical presentations of Parkinson’s disease. Journal of Alzheimer’s Disease, 81(1), 201–207. https://doi.org/10.3233/JAD-210408
UCSF Memory and Aging Center. (n.d.). Parkinson’s disease dementia. https://memory.ucsf.edu/dementia/parkinsons/parkinson-disease-dementia
University of Rochester Medical Center. (n.d.). Anatomy of the brain. https://www.urmc.rochester.edu/encyclopedia/content.aspx?ContentTypeID=85&ContentID=P00773
World Health Organization. (2023, March 15). Dementia. https://www.who.int/news-room/fact-sheets/detail/dementia
Xie, A., Gao, J., Xu, L., & Meng, D. (2014). Shared mechanisms of neurodegeneration in Alzheimer’s disease and Parkinson’s disease. BioMed Research International, 2014. https://doi.org/10.1155/2014/717419
Yücelbaş, C. (2021). A new approach: Information gain algorithm-based k-nearest neighbors hybrid diagnostic system for Parkinson’s disease. Physical and Engineering Sciences in Medicine, 44(2), 511–524. https://doi.org/10.1007/s13246-021-01001-4
Zegers, C., Posch, J., Traverso, A., Eekers, D., Postma, A., Backes, W., Dekker, A., & van Elmpt, W. (2021). Current applications of deep-learning in neuro-oncological MRI. Physica Medica, 83, 161–173. https://doi.org/10.1016/j.ejmp.2021.03.007
Zesiewicz, T. A., Baker, M. J., Wahba, M., & Hauser, R. A. (2003). Autonomic nervous system dysfunction in Parkinson’s disease. Current Treatment Options in Neurology, 5(2), 149–160. https://doi.org/10.1007/s11940-003-0021-6
Ali, M. A., Ahammad, M., Nawshad, N., Shifat, S. M., Mridha, M. F., Ahmmed, F., & Jannat Tania, N. A. (2025). ParkinNet: a Novel Approach to Classifying Alzheimer’s and Parkinson’s Diseases Using Brain Structural MRI. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 14, e32178. https://doi.org/10.14201/adcaij.32178
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