A Novel Deep Convolutional Neural Network Model for Detection of Parkinson Disease by Analysing the Spiral Drawing

Islam, Md. Rakibul; Matin, Abdul; Nahiduzzaman, Md.; Siddiquee, Md. Saifullah; Hasnain, Fahim Md. Sifnatul; Shovan, S.M.; Hasan, Tonmoy

doi:10.1007/978-981-16-0586-4_13

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…Islam et al [ 22 ] applied a four-convolutional and two fully connected layers model to obtain the detection of PD based on spiral drawing. The CNN layers were responsible for extracting the features of the used images and classifying them.…”

Section: Related Workmentioning

confidence: 99%

Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data

et al. 2022

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Parkinson’s disease (PD) is the most common form of Parkinsonism, which is a group of neurological disorders with PD-like motor impairments. The disease affects over 6 million people worldwide and is characterized by motor and non-motor symptoms. The affected person has trouble in controlling movements, which may affect simple daily-life tasks, such as typing on a computer. We propose the application of a modified SqueezeNet convolutional neural network (CNN) for detecting PD based on the subject’s key-typing patterns. First, the data are pre-processed using data standardization and the Synthetic Minority Oversampling Technique (SMOTE), and then a Continuous Wavelet Transformation is applied to generate spectrograms used for training and testing a modified SqueezeNet model. The modified SqueezeNet model achieved an accuracy of 90%, representing a noticeable improvement in comparison to other approaches.

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Section: Related Workmentioning

confidence: 99%

Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data

et al. 2022

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A Transfer Learning Approach with MobileNetV2 for Parkinson’s Disease Detection using Hand-Drawings

Kumar,

Bansal

2023

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT)

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Recognition analysis of spiral and straight-line drawings in tremor assessment

Jenei,

Sztahó,

Valálik

2024

Biomedical Engineering / Biomedizinische Technik

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Objectives No standard, objective diagnostic procedure exists for most neurological diseases causing tremors. Therefore, drawing tests have been widely analyzed to support diagnostic procedures. In this study, we examine the comparison of Archimedean spiral and line drawings, the possibilities of their joint application, and the relevance of displaying pressure on the drawings to recognize Parkinsonism and cerebellar dysfunction. We further attempted to use an automatic processing and evaluation system. Methods Digital images were developed from raw data by adding or omitting pressure data. Pre-trained (MobileNet, Xception, ResNet50) models and a Baseline (from scratch) model were applied for binary classification with a fold cross-validation procedure. Predictions were analyzed separately by drawing tasks and in combination. Results The neurological diseases presented here can be recognized with a significantly higher macro f1 score from the spiral drawing task (up to 95.7 %) than lines (up to 84.3 %). A significant improvement can be achieved if the spiral is supplemented with line drawing. The pressure inclusion in the images did not result in significant information gain. Conclusions The spiral drawing has a robust recognition power and can be supplemented with a line drawing task to increase the correct recognition. Moreover, X and Y coordinates appeared sufficient without pressure with this methodology.

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A Novel Deep Convolutional Neural Network Model for Detection of Parkinson Disease by Analysing the Spiral Drawing

Cited by 3 publications

References 13 publications

Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data

Modified SqueezeNet Architecture for Parkinson’s Disease Detection Based on Keypress Data

A Transfer Learning Approach with MobileNetV2 for Parkinson’s Disease Detection using Hand-Drawings

Recognition analysis of spiral and straight-line drawings in tremor assessment

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