Abstract
Parkinson’s disease (PD) is typically a neurodegenerative disorder that slowly affects the brain, causes muscle stiffness, limb tremor and impaired balance that tends to get worsen over the time. The early detection of PD is necessary for proper treatment of patients and for providing them better health care services. Computer-aided diagnosis (CAD) system is a non-invasive and low-cost tool which have the potential to help in the diagnosis and monitoring of various diseases. Handwriting is important in the context of PD assessment. For the early detection of this disease, a number of machine learning techniques have been researched. Yet the main problem with the majority of these manual feature extraction methods is their poor performance and accuracy. To deal with this chronic condition, we need a Deep Learning (DL) model that can help in early diagnosis. In order to accomplish this, we propose a hybrid method that incorporates technique for data augmentation, feature extraction with pretrained Convolutional Neural Network (CNN), feature selection using optimization and classification with the help of Machine Learning to enhance PD identification. In this paper firstly, all types of handwriting images (circle, spiral and meander) are fed into six different pretrained models of CNN and are fine-tuned for classification among which VGG16 framework provides the better performance among the others. In the second stage, Binary grey wolf optimization (BGWO) is used for the selection of optimal subset of features extracted from VGG16 network by freezing the layers. The proposed method achieves classification accuracy of 99.8% using Support Vector Machine (SVM). The performance of our approach has been measured over the benchmark NewHandPD dataset. The experimental result shows that the proposed approach detects Parkinson's disease better than state-of-the-art methods by minimizing the feature subsets and thereby maximizing the accuracy.
Similar content being viewed by others
References
Sharma RK, Gupta Anil K (2015) Voice analysis for telediagnosis of Parkinson disease using artificial neural networks and support vector machines. Int J Intell Syst Appl. 7(6):41
Nilashi M, Ibrahim O, Ahani A (2016) Accuracy improvement for predicting Parkinson’s disease progression. Sci Rep 6(1):34181
Johri A, Ashish T (2019) Parkinson disease detection using deep neural networks. 2019 twelfth international conference on contemporary computing (IC3). IEEE.
Monroe T, Carter M (2012) Using the folstein mini mental state exam (MMSE) to explore methodological issues in cognitive aging research. Eur J Ageing 9:265–274
Martinez-Martin P et al (2013) Expanded and independent validation of the movement disorder society–unified Parkinson’s disease rating scale (MDS-UPDRS). J Neurol. 260:228–236
Aich S, et al. (2018) A nonlinear decision tree-based classification approach to predict the Parkinson’s disease using different feature sets of voice data. 2018 20th international conference on advanced communication technology (ICACT). IEEE.
Millian-Morell L et al (2018) Relations between sensorimotor integration and speech disorders in Parkinson’s disease. Curr Alzheimer Res. 15(2):149–156
Delrobaei M et al (2018) Towards remote monitoring of Parkinson’s disease tremor using wearable motion capture systems. J Neurol Sci. 384:38–45
Xia Y et al (2018) A machine learning approach to detecting of freezing of gait in Parkinson’s disease patients. J Med Imaging Hlth Inform. 8(4):647–654
Ruonala V et al (2018) Levodopa-induced changes in electromyographic patterns in patients with advanced Parkinson’s disease. Front Neurol. 9:35
Kamran I et al (2021) Handwriting dynamics assessment using deep neural network for early identification of Parkinson’s disease. Future Gen Comput Syst. 117:234–244
Pereira CR, et al. (2015) A step towards the automated diagnosis of parkinson’s disease: analyzing handwriting movements. 2015 IEEE 28th international symposium on computer-based medical systems. IEEE.
Diaz M et al (2019) Dynamically enhanced static handwriting representation for Parkinson’s disease detection. Pattern Recogn Lett. 128:204–210
Xiao Z et al (2021) A federated learning system with enhanced feature extraction for human activity recognition. Knowl Based Syst. 229:107338
Li X et al (2021) A combined loss-based multiscale fully convolutional network for high-resolution remote sensing image change detection. IEEE Geosci Remote Sens Lett. 19:1–5
Huynh BQ, Li H, Giger ML (2016) Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging 3(3):034501–034501
Balaha HM et al (2021) Recognizing arabic handwritten characters using deep learning and genetic algorithms. Multimed Tools Appl. 80:32473–32509
Pereira CR, et al. (2016) Deep learning-aided Parkinson's disease diagnosis from handwritten dynamics. 2016 29th SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE.
Impedovo D (2019) Velocity-based signal features for the assessment of Parkinsonian handwriting. IEEE Signal Process Lett 26(4):632–636
Naseer A et al (2020) Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput Appl. 32:839–854
Gazda M, Máté H, Peter D (2022) Ensemble of convolutional neural networks for Parkinson’s disease diagnosis from offline handwriting.
Mohaghegh M, Gascon J (2021) Identifying Parkinson’s disease using multimodal approach and deep learning. In: Proc. 6th Int. Conf. Innov. Technol. Intell. Syst. Ind. Appl. (CITISIA). p 1–6.
Krishna A et al (2021) Speech parameter and deep learning based approach for the detection of Parkinson’s disease. Computer networks, big data and IoT: proceedings of ICCBI 2020. Springer, Singapore
Abayomi-Alli OO, et al. (2020) BiLSTM with data augmentation using interpolation methods to improve early detection of parkinson disease. 2020 15th Conference on Computer Science and Information Systems (FedCSIS). IEEE.
Bhagat M, Kumar D, Kumar S (2023) Bell pepper leaf disease classification with LBP and VGG-16 based fused features and RF classifier. Int J Inf Technol 15(1):465–475
Yang X-S (2010) A new metaheuristic bat-inspired algorithm. Nature inspired cooperative strategies for optimization (NICSO 2010). p 65–74.
Gupta D et al (2020) Usability feature extraction using modified crow search algorithm: a novel approach. Neural Comput Appl. 32:10915–10925
Ranjan R, Chhabra JK (2023) Automatic feature selection using enhanced dynamic Crow Search Algorithm. Int J Inform Technol. 1–6. https://doi.org/10.1007/s41870-023-01319-2
Gonzalez-Pardo A, Jung JJ, Camacho D (2017) ACO-based clustering for Ego network analysis. Futur Gener Comput Syst 66:160–170
Sahu B, Mohanty SN (2021) CMBA-SVM: a clinical approach for Parkinson disease diagnosis. Int J Inform Technol. 13(2):647–655
Emary E, Hossam MZ, Aboul EH (2016) Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371–381
Soumaya Z et al (2021) The detection of Parkinson disease using the genetic algorithm and SVM classifier. Appl Acoust. 171:107528
Chapelle O et al (2002) Choosing multiple parameters for support vector machines. Mach Learn. 46:131–159
Pattnaik S, Rout N, Sabut S (2022) Machine learning approach for epileptic seizure detection using the tunable-Q wavelet transform based time–frequency features. Int J Inf Technol 14(7):3495–3505
Sharma A, Pramod KM (2022) Performance analysis of machine learning based optimized feature selection approaches for breast cancer diagnosis. Int J Inform Technol. 1–12. https://doi.org/10.1007/s41870-021-00671-5
Gazda M, Hires M, Drot ˇ ar P (2021) “Multiple-fine-tuned ´ convolutional neural networks for parkinson’s disease diagnosis from offline handwriting. IEEE Trans Syst Man Cybern Syst. 52(1):78–89
Afroz N, Boshir A (2023) Deep transfer learning for early parkinson's disease detection. 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE.
Biswas S, Navpreet K, Seeja KR (2022) Early Detection of Parkinson’s Disease from Hand Drawings Using CNN and LSTM. 2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST). IEEE.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors proclaim that they have no known competing interests that would have seemed to impact on the research presented in this study.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Agrawal, S., Sahu, S.P. Image-based Parkinson disease detection using deep transfer learning and optimization algorithm. Int. j. inf. tecnol. 16, 871–879 (2024). https://doi.org/10.1007/s41870-023-01601-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41870-023-01601-3