Abstract
Machine learning models are being increasingly proposed for the automated classification of Parkinson’s disease from brain imaging data such as magnetic resonance imaging (MRI). However, the problem of class imbalance is a major setback in deriving the maximum benefit from using these techniques. We propose in overcoming the class imbalance between Parkinson’s disease (PD) and normal cohorts (NC) subjects by using variational autoencoders (VAEs) as the generative model that follows a probabilistic regeneration and aim to experiment over the effect of latent variables in generating new MR images of the subjects to improve the detection of PD. The efficiency of the proposed method with and without data augmentation is compared and evaluated using a deep learning classifier model at the subject level. The results obtained using a model trained with data augmentation show a significant increase of 6% in the performance of the classification model.
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References
Pinter, B., et al.: Mortality in Parkinson’s disease: a 38-year follow-up study. Mov. Disord. 30(2), 266–269 (2015)
Kose, U., Deperlioglu, O., Alzubi, J., Patrut, B.: Diagnosing parkinson by using deep autoencoder neural network. In: Deep Learning for Medical Decision Support Systems. Studies in Computational Intelligence, vol 909. Springer, Singapore (2021)
Pyatigorskaya, N., et al.: A review of the use of magnetic resonance imaging in Parkinson’s disease. Ther. Adv. Neurol. Disord. 7(4), 206–220 (2014)
Shinde, S., et al.: Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI. NeuroImage: Clin. 22, 101748 (2019)
Xu, Q., et al.: Different latent variables learning in variational autoencoder. In: 2017 4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS). IEEE (2017)
Harini, N., Ramji, B., Sriram, S., Sowmya, V., Soman, K.: Musculoskeletal radiographs classification using deep learning. In: Deep Learning for Data Analytics, pp. 79-98. Elsevier (2020)
Gopika, P., Sowmya, V., et al.: Transferable approach for cardiac disease classification using deep learning. In: Deep Learning Techniques for Biomedical and Health Informatics, pp. 285–303. Academic Press (2020)
Simon, A., Ravi, V., Vishvanathan, S., Sowmya, K.P.: Shallow CNN with LSTM layer for tuberculosis detection in microscopic image. Int. J. Rec. Technol. Eng. 7, 56–60 (2019)
Sivaranjini, S., Sujatha, C.M.: Deep learning based diagnosis of Parkinson’s disease using convolutional neural network. Multimed. Tools Appl. 79(21), 15467–15479 (2020)
Rajanbabu, K., Veetil, I.K., Sowmya, V., Gopalakrishnan, E.A., Soman, K.P.: Ensemble of deep transfer learning models for parkinson’s disease classification. In: Accepted in Fourth International Conference Soft Computing and Signal Processing (ICSCSP) (2020)
Madan, Y., Veetil, I.K., et al.: Deep learning based approach for Parkinson’s Disease Detection using Region of Interest. In: Accepted in 4th International Conference on Intelligent Sustainable Systems (ICISS) (2021)
Bhateja, V., et al.: Haralick features-based classification of mammograms using SVM. In: Information Systems Design and Intelligent Applications, pp. 787–795. Springer, Singapore (2018)
Balasubramanian, R., et al.: Analysis of adversarial based augmentation for diabetic retinopathy disease grading. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE (2020)
Qiang, N., Dong, Q., Sun, Y., Ge, B., Liu, T.: deep variational autoencoder for modeling functional brain networks and ADHD identification. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 554–557 (2020)
Fedorov, A., et al.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imag. 30(9), 1323–1341 (2012)
McCarthy, P.: FSLeyes (2020)
Islam, J., Zhang, Y.: GAN-based synthetic brain PET image generation. Brain Inform. 7, 1–12 (2020)
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Madan, Y., Veetil, I.K., V, S., EA, G., KP, S. (2022). Synthetic Data Augmentation of MRI using Generative Variational Autoencoder for Parkinson’s Disease Detection. In: Bhateja, V., Tang, J., Satapathy, S.C., Peer, P., Das, R. (eds) Evolution in Computational Intelligence. Smart Innovation, Systems and Technologies, vol 267. Springer, Singapore. https://doi.org/10.1007/978-981-16-6616-2_16
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DOI: https://doi.org/10.1007/978-981-16-6616-2_16
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