classification

Rate-accuracy trade-off in video classification with deep convolutional neural networks

Abbas, A; Chadha, A; Andreopoulos, Y; Jubran, M; (2018) Rate-accuracy trade-off in video classification with deep convolutional neural networks. In: (Proceedings) 2018 25th IEEE International Conference on Image Processing (ICIP)....

Read more
[Pulmonary Hypertension: Diagnostics, Classification and Therapy].

[ad_1] Related Articles [Pulmonary Hypertension: Diagnostics, Classification and Therapy]. Anasthesiol Intensivmed Notfallmed Schmerzther. 2019 May;54(5):320-333 Authors: Lücke E, Schmeißer A, Schreiber J Abstract Pulmonary hypertension is a chronic, incurable disease...

Read more
How to Develop a Convolutional Neural Network From Scratch for CIFAR-10 Photo Classification

[ad_1] The CIFAR-10 small photo classification problem is a standard dataset used in computer vision and deep learning. Although the dataset is effectively solved, it can be used as the...

Read more
How to Develop a Deep Convolutional Neural Network From Scratch for Fashion MNIST Clothing Classification

[ad_1] The Fashion-MNIST clothing classification problem is a new standard dataset used in computer vision and deep learning. Although the dataset is relatively simple, it can be used as the...

Read more
How to Develop a Convolutional Neural Network From Scratch for MNIST Handwritten Digit Classification

[ad_1] The MNIST handwritten digit classification problem is a standard dataset used in computer vision and deep learning. Although the dataset is effectively solved, it can be used as the...

Read more
Architectural Innovations in Convolutional Neural Networks for Image Classification

[ad_1] A Gentle Introduction to the Innovations in LeNet, AlexNet, VGG, Inception, and ResNet Convolutional Neural Networks. Convolutional neural networks are comprised of two very simple elements, namely convolutional layers...

Read more
How to Use Test-Time Augmentation to Improve Model Performance for Image Classification

[ad_1] Data augmentation is a technique often used to improve performance and reduce generalization error when training neural network models for computer vision problems. The image data augmentation technique can...

Read more
Clinical text classification with rule-based features and knowledge-guided convolutional neural networks.

[ad_1] Related Articles Clinical text classification with rule-based features and knowledge-guided convolutional neural networks. BMC Med Inform Decis Mak. 2019 Apr 04;19(Suppl 3):71 Authors: Yao L, Mao C, Luo Y...

Read more
Video Classification with Channel-Separated Convolutional Networks. (arXiv:1904.02811v1 [cs.CV])

[ad_1] Group convolution has been shown to offer great computational savings in various 2D convolutional architectures for image classification. It is natural to ask: 1) if group convolution can help...

Read more
How to Evaluate Pixel Scaling Methods for Image Classification With Convolutional Neural Networks

[ad_1] Image data must be prepared before it can be used as the basis for modeling in image classification tasks. One aspect of preparing image data is scaling pixel values,...

Read more
Particle swarm optimization for network-based data classification.

[ad_1] Related Articles Particle swarm optimization for network-based data classification. Neural Netw. 2019 Feb;110:243-255 Authors: Carneiro MG, Cheng R, Zhao L, Jin Y Abstract Complex networks provide a powerful tool...

Read more
Expert-level classification of ventilatory thresholds from cardiopulmonary exercising test data with recurrent neural networks.

[ad_1] Related Articles Expert-level classification of ventilatory thresholds from cardiopulmonary exercising test data with recurrent neural networks. Eur J Sport Sci. 2019 Mar 18;:1-9 Authors: Zignoli A, Fornasiero A, Stella...

Read more
Development of a deep neural network for automated electromyographic pattern classification [METHODS AND TECHNIQUES]

Riad Akhundov, David J. Saxby, Suzi Edwards, Suzanne Snodgrass, Phil Clausen, and Laura E. Diamond Determining the signal quality of surface electromyography (sEMG) recordings is time consuming and requires the...

Read more
Using classification techniques for statistical analysis of Anemia.

[ad_1] Related Articles Using classification techniques for statistical analysis of Anemia. Artif Intell Med. 2019 Mar;94:138-152 Authors: Meena K, Tayal DK, Gupta V, Fatima A Abstract Anemia in children is...

Read more
Multi-label Biomedical Question Classification for Lexical Answer Type Prediction.

[ad_1] Related Articles Multi-label Biomedical Question Classification for Lexical Answer Type Prediction. J Biomed Inform. 2019 Mar 11;:103143 Authors: Wasim M, Asim MN, Ghani Khan MU, Mahmood W Abstract Question...

Read more
Encodings and models for antimicrobial peptide classification for multi-resistant pathogens.

[ad_1] Related Articles Encodings and models for antimicrobial peptide classification for multi-resistant pathogens. BioData Min. 2019;12:7 Authors: Spänig S, Heider D Abstract Antimicrobial peptides (AMPs) are part of the inherent...

Read more
Periodontal diagnosis in the context of the 2017 classification system of periodontal diseases and conditions: presentation of a patient with periodontitis localised to the molar teeth

[ad_1] Wadia, R; Walter, C; Chapple, ILC; Ower, P; Tank, M; West, NX; Needleman, I; ... Dietrich, T; + view all Wadia, R; Walter, C; Chapple, ILC; Ower, P; Tank,...

Read more
netDx: interpretable patient classification using integrated patient similarity networks

Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data...

Read more
Convolutional neural network for cell classification using microscope images of intracellular actin networks.

[ad_1] Related Articles Convolutional neural network for cell classification using microscope images of intracellular actin networks. PLoS One. 2019;14(3):e0213626 Authors: Oei RW, Hou G, Liu F, Zhong J, Zhang J,...

Read more
Unsupervised Classification of Multi-Omics Data during Cardiac Remodeling using Deep Learning.

[ad_1] Related Articles Unsupervised Classification of Multi-Omics Data during Cardiac Remodeling using Deep Learning. Methods. 2019 Mar 07;: Authors: Christopher Chung N, Mirza B, Choi H, Wang J, Wang D,...

Read more