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Current Medical Imaging

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ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

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Research Article

Modified Exigent Features Block in JAN Net for Analysing SPECT Scan Images to Diagnose Early-Stage Parkinson’s Disease

Author(s): Jothi Siluvaimuthu, Anita Sebasthiyar and Sivakumar Subburam*

Volume 20, 2024

Published on: 02 August, 2023

Article ID: e050623217645 Pages: 11

DOI: 10.2174/1573405620666230605092654

open_access

Abstract

Background: The quantitative measure of dopamine transporter (DaT) in the human midbrain is generally used as a biomarker for analyzing Parkinson’s disease (PD).

Introduction: DaT scan images or Single- photon emission computed tomography (SPECT) images are utilized to capture the dopamine content more accurately.

Methods: Only sixteen slices out of ninety-one of SPECT images were chosen on the basis of the high amount of dopamine content and were named Volume rendering image slices (VRIS). This paper proposes a novel Convolutional Neural Network (CNN) called JAN Net which particularly treats the VRIS for identifying PD. The JAN Net preserves the edges and spatial features of the striatum by using a modified exigent feature (M-ExFeat) block, that contains convolutional and additive layer. The different-sized convolutional layer extracts both low- and high-level features of Striatum. The additive layer adds up all the features of different filter sized convolutional layers like 1x1, 3x3, and 5x5. The added output features are used to improve the learnability of neurons in the hidden layer. The network performance is tested for stride 1 and stride 2.

Results: The results are validated using the dataset taken from the Parkinson’s Progression Markers Initiative (PPMI) database. The JAN Net ensures improved performance in terms of accuracy. The training and validation accuracy for stride 2 is 100% with minimum losses. The outcome has been compared with different deep learning architectures and the machine learning techniques like Extreme Learning Machines (ELM), and Artificial Neural Networks (ANN) to highlight the efficacy of the proposed architecture.

Conclusion: Hence, the present work could be of great aid to the experts in neurology to protect the neurons from impairment.

Keywords: Early PD, CNN, Modified exfeat, Performance, JAN network, Artificial intelligence.

[1]
de Lau LML, Breteler MMB. Epidemiology of Parkinson’s disease. Lancet Neurol 2006; 5(6): 525-35.
[http://dx.doi.org/10.1016/S1474-4422(06)70471-9] [PMID: 16713924]
[2]
DeMaagd G, Philip A. Parkinson’s disease and its management: Part 1: Disease entity, risk factors, pathophysiology, clinical presentation, and diagnosis. P T 2015; 40(8): 504-32.
[PMID: 26236139]
[3]
Pringsheim T, Jette N, Frolkis A, Steeves TDL. The prevalence of Parkinson’s disease: A systematic review and meta-analysis. Mov Disord 2014; 29(13): 1583-90.
[http://dx.doi.org/10.1002/mds.25945] [PMID: 24976103]
[4]
Armstrong MJ, Okun MS. Diagnosis and treatment of Parkinson disease: A review. JAMA 2020; 323(6): 548-60.
[http://dx.doi.org/10.1001/jama.2019.22360] [PMID: 32044947]
[5]
Ball N, Teo WP, Chandra S, Chapman J. Parkinson’s disease and the environment. Front Neurol 2019; 10: 218.
[http://dx.doi.org/10.3389/fneur.2019.00218] [PMID: 30941085]
[6]
Korolev S, Safiullin A, Belyaev M, Dodonova Y. Residual and plain convolutional neural networks for 3D brain MRI classification. IEEE International Symposium on Biomedical Imaging. Melbourne, VIC, Australia, 18-21 April 2017, pp.835-838.
[http://dx.doi.org/10.1109/ISBI.2017.7950647]
[7]
Marek K, Jennings D, Lasch S, et al. The Parkinson Progression Marker Initiative (PPMI). Prog Neurobiol 2011; 95(4): 629-35.
[http://dx.doi.org/10.1016/j.pneurobio.2011.09.005] [PMID: 21930184]
[8]
Prashanth R, Dutta Roy S, Ghosh S, Pravat Mandal K. Shape features as biomarkers in early Parkinson’s disease. 6th International IEEE/EMBS Conference on Neural Engineering (NER). San Diego, CA, US, 06-08 November 2014.
[9]
Francisco PMO, Castelo-Branco M. Computer-aided diagnosis of Parkinson’s disease based on [123I] FP-CIT SPECT binding potential images, using the voxels-as-features approach and support vector. J Neural Eng 2015; 12: 10.
[10]
Anita S, Aruna Priya P. Diagnosis of parkinson’s disease at an early stage using volume rendering spect image slices. Arab J Sci Eng 2020; 45(4): 2799-811.
[http://dx.doi.org/10.1007/s13369-019-04152-7]
[11]
Anita S, Priya PA. Three dimensional analysis of SPECT Images for diagnosing early Parkinson’s disease using radial basis function kernel − Extreme learning machine. Curr Med Imaging Rev 2019; 15(5): 461-70.
[http://dx.doi.org/10.2174/1573405614666171219154154] [PMID: 32008553]
[12]
Krizhevsky A, Suskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012; 25: 1097-105.
[13]
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521(7553): 436-44.
[http://dx.doi.org/10.1038/nature14539] [PMID: 26017442]
[14]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv 2014; 2014: 1556.
[15]
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. IEEE conference on computer vision and pattern recognition. Las Vegas, NV, USA, 27-30 June 2016, pp.770-778.
[http://dx.doi.org/10.1109/CVPR.2016.90]
[16]
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. IEEE conference on computer vision and pattern recognition. Las Vegas, NV, USA, 27-30 June 2016, pp.2818-2826.
[http://dx.doi.org/10.1109/CVPR.2016.308]
[17]
Ortiz A, Munilla J, Martínez-Ibañez M, Górriz JM, Ramírez J, Salas-Gonzalez D. Parkinson’s disease detection using isosurfaces-based features and convolutional neural networks. Front Neuroinform 2019; 13: 48.
[http://dx.doi.org/10.3389/fninf.2019.00048] [PMID: 31312131]
[18]
The study that could change everything. 2010. Available From: https://www.ppmi-info.org
[19]
Gil-Martín M, Montero JM, San-Segundo R. Parkinson’s disease detection from drawing movements using convolutional neural networks. electronics 2019; 8(8): 907.
[http://dx.doi.org/10.3390/electronics8080907]
[20]
Verma M, Bhui JK, Vipparthi SK, Singh G. EXPERTNet: Exigent features preservative network for facial expression recognition. 11th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP 2018). New York, NY, USA. 2018; pp. 1-8.
[http://dx.doi.org/10.1145/3293353.3293374]
[21]
Masood S, Sharif M, Masood A, Yasmin M, Raza M. A survey on medical image segmentation. Curr Med Imaging Rev 2015; 11(1): 3-14.
[http://dx.doi.org/10.2174/157340561101150423103441]
[22]
Djang DSW, Janssen MJR, Bohnen N, et al. SNM practice guideline for dopamine transporter imaging with 123I-ioflupane SPECT 1.0. J Nucl Med 2012; 53(1): 154-63.
[http://dx.doi.org/10.2967/jnumed.111.100784] [PMID: 22159160]
[23]
Barbero-Gómez J, Gutiérrez P-A, Vargas V-M, Vallejo-Casas J-A, Hervás-Martínez C. An ordinal CNN approach for the assessment of neurological damage in Parkinson’s disease patients. Expert Syst Appl 2021; 182: 115271.
[http://dx.doi.org/10.1016/j.eswa.2021.115271]
[24]
Choi H, Ha S, Im HJ, Paek SH, Lee DS. Refining diagnosis of Parkinson’s disease with deep learning-based interpretation of dopamine transporter imaging. Neuroimage Clin 2017; 16: 586-94.
[http://dx.doi.org/10.1016/j.nicl.2017.09.010] [PMID: 28971009]
[25]
Pianpanit T, Lolak S, Sawangjai P, Sudhawiyangkul T, Wilaiprasitporn T. Parkinson’s disease recognition using SPECT image and interpretable AI: A tutorial. IEEE Sensors J 2021; 99
[26]
Nikhil JD, Thomopoulos SI, Owens-Walton C, et al. 3D Convolutional neural networks for classification of Alzheimer’s and Parkinson’s disease with T1-weighted brain MRI. bioRxiv 2021; 2021: 453903.
[http://dx.doi.org/10.1101/2021.07.26.453903]
[27]
Wolfswinkel Ev, Wielaard J, Lavalaye J. Artificial intelligence-based assistance in clinical 123I-FP-CIT SPECT scan interpretation. Res Square 2021.
[http://dx.doi.org/10.21203/rs.3.rs-721186/v1]
[28]
Mohammed F, He X, Lin Y. Retracted: An easy-to-use deep-learning model for highly accurate diagnosis of Parkinson’s disease using SPECT images. Comput Med Imaging Graph 2021; 87: 101810.
[http://dx.doi.org/10.1016/j.compmedimag.2020.101810] [PMID: 33279760]

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