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

Gil-Martín, Manuel; Montero, Juan Manuel; San-Segundo, Rubén

doi:10.3390/electronics8080907

Cited by 91 publications

(49 citation statements)

References 34 publications

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“…CNN is one of the best Machine-Learning (ML) algorithms that is used in analyzing medical images, since it maintains the spatial relationships after filtering the input images. These relationships are extremely significant in the field of medical analysis [57][58][59]. CNN consists of multiple layers, as shown in Figure 8.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

et al. 2020

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Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a reduction in the breast cancer death rate. With the help of a computer-aided diagnosis system, the efficiency increased, and the cost was reduced for the cancer diagnosis. Traditional breast cancer classification techniques are based on handcrafted features techniques, and their performance relies upon the chosen features. They also are very sensitive to different sizes and complex shapes. However, histopathological breast cancer images are very complex in shape. Currently, deep learning models have become an alternative solution for diagnosis, and have overcome the drawbacks of classical classification techniques. Although deep learning has performed well in various tasks of computer vision and pattern recognition, it still has some challenges. One of the main challenges is the lack of training data. To address this challenge and optimize the performance, we have utilized a transfer learning technique which is where the deep learning models train on a task, and then fine-tune the models for another task. We have employed transfer learning in two ways: Training our proposed model first on the same domain dataset, then on the target dataset, and training our model on a different domain dataset, then on the target dataset. We have empirically proven that the same domain transfer learning optimized the performance. Our hybrid model of parallel convolutional layers and residual links is utilized to classify hematoxylin–eosin-stained breast biopsy images into four classes: invasive carcinoma, in-situ carcinoma, benign tumor and normal tissue. To reduce the effect of overfitting, we have augmented the images with different image processing techniques. The proposed model achieved state-of-the-art performance, and it outperformed the latest methods by achieving a patch-wise classification accuracy of 90.5%, and an image-wise classification accuracy of 97.4% on the validation set. Moreover, we have achieved an image-wise classification accuracy of 96.1% on the test set of the microscopy ICIAR-2018 dataset.

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Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

et al. 2020

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“…In some studies, the authors use touch screen data from several users of smart phones in order to detect PD [6,7]. Meanwhile in others, the authors use drawing movements as a biomarker to determine the motor impairments related to early PD symptoms [8]. Another technique is the detection of PD by finger typing on a usual keyboard [9,10].…”

Section: Introductionmentioning

confidence: 99%

Detection of early Parkinson’s disease with wavelet features using finger typing movements on a keyboard

2020

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This study presents a new Parkinson's disease diagnosis technique based on wavelets extracted features and machine learning paradigms. The present-day diagnosis techniques suffer from low diagnosis accuracy and also require the patient to go to a medical facility, where the diagnosis is done by a specialist. In this work, we propose an automatic diagnosis method where by, all the patient has to do is to type some keys on their keyboard, and the algorithm will calculate the latency time, flight time and hold time of each key pressed, to make a diagnosis of Parkinson's disease. We use several wavelets to extract some features that are classified into Parkinson's disease or non-Parkinson's disease. The results are very encouraging and we obtain a classification accuracy of up to 100% in some of the cases, using a tenfold crossvalidation technique. Wavelets are a tool that can be used to complement and improve the detection of Parkinson's disease. These results will permit the amelioration of some state-of-the-art methods which use a similar technique to detect Parkinson's disease.

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“…The advantages of artificial intelligence (AI) have brought many achievements to the healthcare field in both industry and academia [1]. For example, in recent years, as a research hotspot, convolutional neural networks (CNN) have been used in healthcare to automatically diagnosis diseases [2]. For AI healthcare, clinical examination is critical in diagnosing and reducing diseases.…”

Section: Introductionmentioning

confidence: 99%

StoolNet for Color Classification of Stool Medical Images

2019

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The color classification of stool medical images is commonly used to diagnose digestive system diseases, so it is important in clinical examination. In order to reduce laboratorians’ heavy burden, advanced digital image processing technologies and deep learning methods are employed for the automatic color classification of stool images in this paper. The region of interest (ROI) is segmented automatically and then classified with a shallow convolutional neural network (CNN) dubbed StoolNet. Thanks to its shallow structure and accurate segmentation, StoolNet can converge quickly. The sufficient experiments confirm the good performance of StoolNet and the impact of the different training sample numbers on StoolNet. The proposed method has several advantages, such as low cost, accurate automatic segmentation, and color classification. Therefore, it can be widely used in artificial intelligence (AI) healthcare.

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

Cited by 91 publications

References 34 publications

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model

Detection of early Parkinson’s disease with wavelet features using finger typing movements on a keyboard

StoolNet for Color Classification of Stool Medical Images

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