A Deep Learning-CNN Based System for Medical Diagnosis: An Application on Parkinson’s Disease Handwriting Drawings

Khatamino, Pedram; Cantürk, İsmail; Ozyilmaz, L.

doi:10.1109/ceit.2018.8751879

Cited by 65 publications

(39 citation statements)

References 17 publications

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“…The optimizer was fixed to the root-mean-square propagation method. This optimizer also reported the best results in Khatamino et al's work [26].…”

Section: Convolutional Neural Networkmentioning

confidence: 56%

“…When considering previous studies over the same dataset, Gallicchio et al [25] obtained an accuracy of 89.3% while Khatamino et al [26] reported an accuracy of 72.5% for the subject-wise crossvalidation. We used a CNN with a similar structure to the CNN used by Khatamino et al [26] but the These results are considerably better than those reported in previous works with different datasets but using also spiral drawings: Kotsavasiloglou et al [24] obtained an accuracy of 88.63% and an AUC of 93.1% while Zham et al [15] reported an accuracy of 83.2% and an AUC of 93.3%. When considering previous studies over the same dataset, Gallicchio et al [25] obtained an accuracy of 89.3% while Khatamino et al [26] reported an accuracy of 72.5% for the subject-wise cross-validation.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The percentage of deactivated weights was 20%. This architecture was inspired by Khatamino et al's work [26] where the authors proposed a simplification of the AlexNet CNN [28]. This simplification was necessary because a smaller dataset was used to train the CNN parameters.…”

Section: Signal Preprocessingmentioning

confidence: 99%

“…Gallicchio et al [25] proposed the use of DeepESNs obtaining an accuracy of 89.3%. Khatamino et al [26] used a convolution neural network (CNN) inspired in the AlexNet [28] composed of two main parts (convolutional layers for feature extraction and full connected layers for classification). Khatamino et al [26] considered a simplified version (a smaller number of layers) because a smaller dataset was used to train the CNN parameters.…”

mentioning

confidence: 99%

“…Khatamino et al [26] used a convolution neural network (CNN) inspired in the AlexNet [28] composed of two main parts (convolutional layers for feature extraction and full connected layers for classification). Khatamino et al [26] considered a simplified version (a smaller number of layers) because a smaller dataset was used to train the CNN parameters. In this work, we use also a simplified version of the AlexNet but with an important difference: The inputs to the CNN are the spectrum points instead of the raw data directly.…”

mentioning

confidence: 99%

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Parkinson’s Disease Detection from Drawing Movements Using Convolutional Neural Networks

2019

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Nowadays, an important research effort in healthcare biometrics is finding accurate biomarkers that allow developing medical-decision support tools. These tools help to detect and supervise illnesses like Parkinson’s disease (PD). This paper contributes to this effort by analyzing a convolutional neural network (CNN) for PD detection from drawing movements. This CNN includes two parts: feature extraction (convolutional layers) and classification (fully connected layers). The inputs to the CNN are the module of the Fast Fourier’s transform in the range of frequencies between 0 Hz and 25 Hz. We analyzed the discrimination capability of different directions during drawing movements obtaining the best results for X and Y directions. This analysis was performed using a public dataset: Parkinson Disease Spiral Drawings Using Digitized Graphics Tablet dataset. The best results obtained in this work showed an accuracy of 96.5%, a F1-score of 97.7%, and an area under the curve of 99.2%.

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“…The optimizer was fixed to the root-mean-square propagation method. This optimizer also reported the best results in Khatamino et al's work [26].…”

Section: Convolutional Neural Networkmentioning

confidence: 56%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Signal Preprocessingmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Parkinson’s Disease Detection from Drawing Movements Using Convolutional Neural Networks

2019

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Early Detection of Parkinson’s Disease from Micrographic Static Hand Drawings

et al. 2021

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Parkinson's disease (PD) is a neurological illness that occurs by the degeneration of cells in the nervous system. Early symptoms include tremors or involuntary movements of the hands, arms, legs, and jaw. Currently, the only method to diagnose PD involves the observation of its prodromal symptoms. Moreover, detecting handwriting will work as a variable for clinitians to understand PD in patients better. With the advancement of technology, it is possible to build applications that will aid in diagnosing PD without any clinical intervention. The majority suffering from PD have handwriting abnormalities (referred to as micrographia), which is the most reported among earlier signs of the disease. So this research is undertaken by focusing on the implication of micrographia. For this purpose, handwritten images are collected from a group of 136 PD patients and 36 healthy patients. These images form a dataset of 800 images that are used to train a model which will accurately classify PD patients. To achieve this transfer learning is chosen because of its ability to produce accurate results regardless of the limited size of the dataset. Here, different models of transfer learning are trained to figure out the well-fitting model. It was observed that VGG-16 performed adequately with a training accuracy of 90.63% while a testing accuracy of 91.36%.

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Transfer Learning and Pressure Effect for Handwriting to Early Detection of Parkinson’s Disease

El Ghzizal,

Aharrane,

Khaissidi

et al. 2022

Digital Technologies and Applications

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A Deep Learning-CNN Based System for Medical Diagnosis: An Application on Parkinson’s Disease Handwriting Drawings

Cited by 65 publications

References 17 publications

Parkinson’s Disease Detection from Drawing Movements Using Convolutional Neural Networks

Parkinson’s Disease Detection from Drawing Movements Using Convolutional Neural Networks

Early Detection of Parkinson’s Disease from Micrographic Static Hand Drawings

Transfer Learning and Pressure Effect for Handwriting to Early Detection of Parkinson’s Disease

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