International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 14, No. 2, 8 Apr. 2022
Cover page and Table of Contents: PDF (size: 497KB)
Non-invasive Detection of Parkinson's Disease Using Deep Learning
Full Text (PDF, 497KB), PP.38-46
Views: 0 Downloads: 0
Author(s)
Chiranji Lal Chowdhary
1,*
R. Srivatsan
1,2
Index Terms
Parkinson's disease, Convolutional Neural Network, specific single-photon emission computerized tomography, dopamine transporter, histogram of oriented gradients
Abstract
Being a near end to a confident life, there is no simple test to diagnose stages of patients with Parkinson's disease (PD) for a patient. In order to estimate whether the disease is in control and to check if medications are regulated, the stage of the disease must be able to be determined at each point. Clinical techniques like the specific single-photon emission computerized tomography (SPECT) scan called a dopamine transporter (DAT) scan is expensive to perform regularly and may limit the patient from getting regular progress of his body. The proposed approach is a lightweight computer vision method to simplify the detection of PD from spirals drawn by the patients. The customized architecture of convolutional neural network (CNN) and the histogram of oriented gradients (HoG) based feature extraction. This can progressively aid early detection of the disease provisioning to improve the future quality of life despite the threatening symptoms by ensuring that the right medication dosages are administered in time. The proposed lightweight model can be readily deployed on embedded and hand-held devices and can be made available to patients for a quick self-examination.
Cite This Paper
Chiranji Lal Chowdhary, R. Srivatsan, " Non-invasive Detection of Parkinson's Disease Using Deep Learning", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.14, No.2, pp. 38-46, 2022. DOI: 10.5815/ijigsp.2022.02.04
Reference
[1] Grover, S., Bhartia, S., Yadav, A. & Seeja, K. R. 2018. Predicting severity of Parkinson’s disease using deep learning. Procedia computer science, 132: 1788-1794.
[2] Sahni, S., Aggarwal, V., Khanna, A., Gupta, D. & Bhattacharyya, S. 2020. Diagnosis of Parkinson’s Disease using a Neural Network based on qPSO. In International Conference on Innovative Computing and Communications, 471-482. Springer, Singapore.
[3] Vyas, K., Vyas, S. & Rajyaguru, N. 2020. Machine Learning Methods for Managing Parkinson’s Disease. In Machine Learning with Health Care Perspective. 263-294. Springer, Cham.
[4] Khare, N., Devan, P., Chowdhary, C. L., Bhattacharya, S., Singh, G., Singh, S. & Yoon, B. 2020. SMO-DNN: Spider Monkey Optimization and Deep Neural Network Hybrid Classifier Model for Intrusion Detection. Electronics, 9(4):692.
[5] Chowdhary, C. L., Goyal, A. & Vasnani, B. K. 2019. Experimental Assessment of Beam Search Algorithm for Improvement in Image Caption Generation. Journal of Applied Science and Engineering, 22(4): 691г698.
[6] Chowdhary, C. L., & Acharjya, D. P. 2020. Segmentation and feature extraction in medical imaging: a systematic review. Procedia Computer Science, 167: 26-36.
[7] Cobbinah, M., Abdulrahman, U. F. I., & Emmanuel, A. K. (2022). Adaptive Neuro-Fuzzy Inferential Approach for the Diagnosis of Prostate Diseases. International Journal of Intelligent Systems & Applications, 14(1).
[8] Bhowmik, P., Bhowmik, P. C., Ali, U. M. E., & Sohrawordi, M. (2021). Cardiotocography Data Analysis to Predict Fetal Health Risks with Tree-Based Ensemble Learning.
[9] Zham, P., Kumar, D. K., Dabnichki, P., Poosapadi Arjunan, S. & Raghav, S. 2017. Distinguishing different stages of Parkinson’s disease using composite index of speed and pen-pressure of sketching a spiral. Frontiers in Neurology, 8: 435.
[10] Rumman, M., Tasneem, A. N., Farzana, S., Pavel, M. I. & Alam, M. A. 2018.. Early detection of Parkinson’s disease using image processing and artificial neural network. In 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 256-261. IEEE.
[11] Schwab, P. & Karlen, W. 2019. PhoneMD: Learning to diagnose Parkinson’s disease from smartphone data. In Proceedings of the AAAI Conference on Artificial Intelligence, 33: 1118-1125.
[12] Surangsrirat, D. & Thanawattano, C. 2012. Android application for spiral analysis in Parkinson's Disease. In 2012 Proceedings of IEEE Southeastcon, 1-6. IEEE.
[13] Ren, M., Pokrovsky, A., Yang, B. & Urtasun, R. 2018. Sbnet: Sparse blocks network for fast inference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 8711-8720.
[14] Bhunia, A. K., Bhowmick, A., Bhunia, A. K., Konwer, A., Banerjee, P., Roy, P. P. & Pal, U. 2018. Handwriting trajectory recovery using end-to-end deep encoder-decoder network. In 2018 24th International Conference on Pattern Recognition (ICPR), 3639-3644. IEEE.
[15] Das, T. K., Chowdhary, C. L. & Gao, X. Z. 2020. Chest X-Ray Investigation: A Convolutional Neural Network Approach. In Journal of Biomimetics, Biomaterials and Biomedical Engineering, 45:57-70). Trans Tech Publications Ltd.
[16] Raveendra, K., & Ravi, J. (2021). Performance evaluation of face recognition system by concatenation of spatial and transformation domain features. International Journal of Computer Network and Information Security, 13(1), 47-60.
[17] Adams, W. R. 2017. High-accuracy detection of early Parkinson's Disease using multiple characteristics of finger movement while typing. PloS one, 12(11).
[18] Hajian-Tilaki, K. 2013. Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian journal of internal medicine, 4(2): 627.
[19] Kolluru, S. H. 2019. Investigations on the inference optimization techniques and their impact on multiple hardware platforms for Semantic Segmentation. arXiv preprint arXiv:1911.12993.
[20] Yamashita, R., Nishio, M., Do, R. K. G. & Togashi, K. 2018. Convolutional neural networks: an overview and application in radiology. Insights into imaging, 9(4): 611-629.
[21] Gil-Martín, M., Montero, J. M. & San-Segundo, R. 2019. Parkinson’s disease detection from drawing movements using convolutional neural networks. Electronics, 8(8): 907.
[22] Isenkul, M., Sakar, B. & Kursun, O. 2014. Improved spiral test using digitized graphics tablet for monitoring Parkinson’s disease. In Proc. of the Int’l Conf. on e-Health and Telemedicine, 171-175.
[23] Sakar, B. E., Isenkul, M. E., Sakar, C. O., Sertbas, A., Gurgen, F., Delil, S., Apaydin, H. & Kursun, O. 2013. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE Journal of Biomedical and Health Informatics, 17(4): 828-834.
[24] Mandrekar, J. N. 2010. Receiver operating characteristic curve in diagnostic test assessment. Journal of Thoracic Oncology, 5(9): 1315-1316.
[25] Foley, J. A., Niven, E. H., Paget, A., Bhatia, K. P., Farmer, S. F., Jarman, P. R., Limousin, P., Warner, T.T., Morris, H.R., Bak, T.H. & Abrahams, S. 2018. Sensitivity and specificity of the ECAS in Parkinson’s disease and progressive supranuclear palsy. Parkinson’s Disease, 2018.
[26] Chowdhary, C. L. 2019. 3D object recognition system based on local shape descriptors and depth data analysis. Recent Patents on Computer Science, 12(1): 18-24.