Improved diagnosis of Parkinson's disease using optimized crow search algorithm

Gupta, Deepak; Sundaram, Shirsh; Khanna, Ashish; Hassanien, Aboul Ella; Albuquerque, Victor Hugo C. de

doi:10.1016/j.compeleceng.2018.04.014

Cited by 160 publications

(52 citation statements)

References 11 publications

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“…Decision tree and knearest neighbor classifier were used on OCFA se-lected feature that diagnose the Parkinsons disease with an accuracy of 94% approximately. Pereira et al applied OSCA (optimal crow search algorithm) for feature selection from handwriting dataset and proposed Random Forest, Decision Tree, k-Nearest Neighbor classifier which gave the 100% prediction rate on OSCA selected features [44].…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

Naseer¹,

Rani²,

Naz³

et al. 2019

Neural Comput & Applic

140

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Parkinson's disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life, however, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson's disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that helps as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that proposed approach provides better detection of Parkinson's disease as compared to state of the art work.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

Naseer¹,

Rani²,

Naz³

et al. 2019

Neural Comput & Applic

140

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“…[16] studied speech recordings using feature extracted from several dimensions of speech, including phonation, articulation and other human characteristics. To improve the diagnosis of Parkinson's disease, [17] introduced an improved and optimized version of the Crow Search Algorithm. Convolutional Neural Networks (CNN) were applied to hand-written exams to investigate their performance of PD detection, achieving 95% of accuracy [18].…”

Section: Related Workmentioning

confidence: 99%

Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques

Almeida

Filho

Carneiro

et al. 2019

Pattern Recognition Letters

Self Cite

201

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This study investigates the processing of voice signals for detecting Parkinson's disease. This disease is one of the neurological disorders that affect people in the world most. The approach evaluates the use of eighteen feature extraction techniques and four machine learning methods to classify data obtained from sustained phonation and speech tasks. Phonation relates to the vowel /a/ voicing task and speech to the pronunciation of a short sentence in Lithuanian language. The audio tasks were recorded using two microphone channels from acoustic cardioid (AC) and a smartphone (SP), thus allowing to evaluate the performance for different types of microphones. Five metrics were employed to analyze the classification performance: Equal Error Rate (EER) and Area Under Curve (AUC) measures from Detection Error Tradeoff (DET) and Receiver Operating Characteristic curves, Accuracy, Specificity, and Sensitivity. We compare this approach with other approaches that use the same data set. We show that the task of phonation was more efficient than speech tasks in the detection of disease. The best performance for the AC channel achieved an accuracy of 94.55%, AUC 0.87, and EER 19.01%. When using the SP channel, we have achieved an accuracy of 92.94%, AUC 0.92, and EER 14.15%.

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“…The data was normalized to prevent issues when using different ML algorithms, such as different ranges for features, unbalanced samples in the dataset, and overfitting the data. This normalization consists of several steps, such as: eliminating duplicate data, resolving conflicting data, and formatting it, converting into a format that allows further processing [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Intelligent Sensory Pen for Aiding in the Diagnosis of Parkinson’s Disease from Dynamic Handwriting Analysis

Júnior

Delmiro

Magaia

et al. 2020

Sensors

Self Cite

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In this paper, we propose a pen device capable of detecting specific features from dynamic handwriting tests for aiding on automatic Parkinson’s disease identification. The method used in this work uses machine learning to compare the raw signals from different sensors in the device coupled to a pen and extract relevant information such as tremors and hand acceleration to diagnose the patient clinically. Additionally, the datasets composed of raw signals from healthy and Parkinson’s disease patients acquired here are made available to further contribute to research related to this topic.

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Improved diagnosis of Parkinson's disease using optimized crow search algorithm

Cited by 160 publications

References 11 publications

RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

RETRACTED ARTICLE: Refining Parkinson’s neurological disorder identification through deep transfer learning

Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques

Intelligent Sensory Pen for Aiding in the Diagnosis of Parkinson’s Disease from Dynamic Handwriting Analysis

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