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u-net-based convolutional neural network  (IEEE Access)
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u-net  (Object Research Systems (ORS) Inc)
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yang digunakan dalam penelitian ini menggunakan metode convolutional neural network berdasarkan arsitektur u net  (Teknik Hizmetler)
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Teknik Hizmetler yang digunakan dalam penelitian ini menggunakan metode convolutional neural network berdasarkan arsitektur u net
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künstliche neuronale netze  (Anwendung GmbH)
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cnn-softmax  (SoftMax Inc)
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bichannel customized cnn  (EyePACS LLC)
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EyePACS LLC bichannel customized cnn
Classification-based studies in DR detection using fundus imaging.
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probability output from cnn  (SoftMax Inc)
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SoftMax Inc probability output from cnn
Classification-based studies in DR detection using fundus imaging.
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cnn software  (RStudio)
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Schematic description of the initial (‘out-of-box’) <t>convolutional</t> <t>neural</t> <t>network</t> <t>(CNN)</t> used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.
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webcam-based eye movement analysis using cnn  (IEEE Access)
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IEEE Access webcam-based eye movement analysis using cnn
Schematic description of the initial (‘out-of-box’) <t>convolutional</t> <t>neural</t> <t>network</t> <t>(CNN)</t> used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.
Webcam Based Eye Movement Analysis Using Cnn, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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cnn  (Kaggle Inc)
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Kaggle Inc cnn
Schematic description of the initial (‘out-of-box’) <t>convolutional</t> <t>neural</t> <t>network</t> <t>(CNN)</t> used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.
Cnn, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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cnn 315  (SoftMax Inc)
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SoftMax Inc cnn 315
Schematic description of the initial (‘out-of-box’) <t>convolutional</t> <t>neural</t> <t>network</t> <t>(CNN)</t> used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.
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1d-cnn  (SoftMax Inc)
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SoftMax Inc 1d-cnn
Schematic description of the initial (‘out-of-box’) <t>convolutional</t> <t>neural</t> <t>network</t> <t>(CNN)</t> used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.
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Image Search Results


Classification-based studies in DR detection using fundus imaging.

Journal: Journal of Imaging

Article Title: Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

doi: 10.3390/jimaging7090165

Figure Lengend Snippet: Classification-based studies in DR detection using fundus imaging.

Article Snippet: Pao, 2020 [ ] , EyePACS , Grade DR based on ICDR scale , Yes , CNN: bichannel customized CNN , 87.83% , 77.81% , 93.88% , 0.93.

Techniques: Imaging, Modification, Extraction

Schematic description of the initial (‘out-of-box’) convolutional neural network (CNN) used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.

Journal: BJA Open

Article Title: Validation of a convolutional neural network that reliably identifies electromyographic compound motor action potentials following train-of-four stimulation: an algorithm development experimental study ☆

doi: 10.1016/j.bjao.2023.100236

Figure Lengend Snippet: Schematic description of the initial (‘out-of-box’) convolutional neural network (CNN) used for the binary classification of a valid compound motor action potential (cMAP) or a non-response. This CNN was modified from a published algorithm for classifying handwritten digits or characters in the Modified National Institute of Standards and Technology (MNIST) dataset. The CNN used for this study consists of a single input, three hidden layers, and two outputs. The input is the raster image of the processed EMG waveform at the adductor pollicis or abductor digiti minimi muscles after electrical stimulation of the ulnar nerve, as described in . The three sequential hidden layers have 512, 256, and 128 nodes with rectified linear unit (relu) activation and a dropout applied after each to reduce overfitting. The model was fit using five epochs with a batch size of 128. The output layer uses softmax activation to assign a probability that the waveform is a valid cMAP or a non-response.

Article Snippet: A CNN in RStudio was built based on a published example of code used to process the Modified National Institute of Standards and Technology (MNIST) image dataset of handwritten digits ( ).

Techniques: Modification, Muscles, Activation Assay