Deep convolutional neural network technology has five primary layers which are named convolution layer, pooling layer, rectified linear unit (ReLU) layer, fully connected layers, and softmax layer. The above steps are performed to define the feature category that satisfies the input image. This study designed and established a PD staging classification model through the pre-trained network in the above manner. Moreover, it evaluated the model effectiveness with accuracy, recall, precision, F-score, and Kappa. NVIDIA GeForce GTX 1060 6 GB (Santa Clara, CA, USA) hardware was used to train the CNN in this study.
Geforce gtx 1060 6 gb
Manufactured by NVIDIA
Sourced in United States
The GeForce GTX 1060 6 GB is a discrete graphics processing unit (GPU) manufactured by NVIDIA. It features 6 GB of GDDR5 video memory and is designed for desktop computers. The GTX 1060 6 GB is capable of rendering high-quality graphics for a variety of applications.
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Lab products found in correlation
2 protocols using geforce gtx 1060 6 gb
1
Parkinson's Disease Staging using CNN
2
Computational Performance Comparison
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Detailed methods are provided in the online version of this paper and include the following: (C) To compare computational performance, we simulate cells with a limited interaction range starting from a spherical, uncompressed, random distribution simulated until the forces were calculated 1,200 times in Chaste (Mirams et al., 2013 ), EmbryoMaker (Marin-Riera et al., 2016) , and yajja (see Figure S1C for the resulting simulations). yajja scales sub-linearly for small systems because of overhead. We use an Intel i7-4770 @ 3.40GHz with an NVidia GeForce GTX 1060 6GB. There would be a parallel implementation of Chaste scaling well for up to 32 CPU cores (Harvey et al., 2015) . Related to Figure S1.
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