scholarly journals Metric Learning Based Convolutional Neural Network for Left-Right Brain Dominance Classification

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Zheng You Lim ◽  
Kok Swee Sim ◽  
Shing Chiang Tan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Metric Learning ◽  
Brain Dominance ◽  
Right Brain

scholarly journals Few-shot cotton leaf spots disease classification based on metric learning

2021 ◽  
Author(s):  
Xihuizi Liang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Leaf Spot ◽  
Classification Accuracy ◽  
Metric Learning ◽  
Small Sample ◽  
Cotton Leaf ◽  
Threshold Segmentation ◽  
Leaf Disease ◽  
Leaf Spots

Abstract Background: Cotton diceases seriously affect the yield and quality of cotton. The type of pest or disease suffered by cotton can be determined by the disease spots on the cotton leaves. This paper presents a small-sample learning framework that can be used for cotton leaf disease spot classification task, which using deep learning techniques is constructed based on a metric learning approach, to prevent and control cotton diseases timely. First, disease spots on cotton leaf's disease images are segmented by different methods, compared by using support vector machine (SVM) method and threshold segmentation, and discussed the suitable one. With segmented disease spot images as input, a disease spot dataset is established, and the cotton leaf disease spots were classified using a classical convolutional neural network classifier, the structure and framework of convolutional neural network had been designed, and the setting of relevant parameters and the detailed network structure configuration are analyzed according to the experimental environment. The features of two different images are extracted by a parallel two-way convolutional neural network with weight sharing. Then, the network uses a loss function to learn the metric space, in which similar leaf samples are close to each other and different leaf samples are far away from each other. Results: To achieve the classification of cotton leaf spots by small sample learning, this paper constructs a metric-based learning method to extract cotton leaf spot features and classify the leaves. In the process of leaf spot extraction, image segmentation of the spots is performed by threshold segmentation and SVM, and comparative analysis is performed. In the process of leaf spot classification, the structural framework of leaf spot feature extractor and feature classifier is constructed, and the overall framework is built using the idea of two-way parallel convolutional neural network. A variety of excellent convolutional neural network feature extractors such as Vgg, DesenNet, and ResNet were used for feature extraction work, and a combination design based on the small sample classification framework was performed and compared. Experimentally, it is demonstrated that the classification accuracy is improved by nearly 7.7% on average for different number of samples in the case of using this optimizer. S-DesneNet have the highest accuracy. When n is 5, 10, 15 and 20, the accuracy is 58.63%, 84.41% ,92.51% and 91.75%, respectively, and the average accuracy is improved by nearly 7.7% compared with DenseNet. Conclusions: To solve the problem of classification accuracy degradation due to small number of samples in small sample training tasks, a spatial structure optimizer (SSO) acting on the training process is proposed for this purpose.

scholarly journals Segmentation-aware convolutional nets

2021 ◽  
Author(s):  
Adam W. Harley
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Metric Learning ◽  
Semantic Segmentation ◽  
Image Point ◽  
Standard Semantic

This thesis introduces a method to both obtain segmentation information and integrate it uniformly within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to produce smooth predictions, which is undesirable for pixel-wise prediction tasks, such as semantic segmentation. The segmentation information is obtained by a form of metric learning, where a CNN learns to compute pixel embeddings that reflect whether any pair of pixels is likely to belong to the same region. This information is then used within a larger network, to replace all convolutions with foreground-focused convolutions, where the foreground is determined adaptively at each image point by local embeddings. The resulting network is called a segmentation-aware CNN, because the network can change its behaviour at each image location according to local segmentation cues. The proposed method yields systematic improvements on a standard semantic segmentation benchmark when compared to a strong baseline.

scholarly journals Segmentation-aware convolutional nets

2021 ◽  
Author(s):  
Adam W. Harley
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Metric Learning ◽  
Semantic Segmentation ◽  
Image Point ◽  
Standard Semantic

This thesis introduces a method to both obtain segmentation information and integrate it uniformly within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to produce smooth predictions, which is undesirable for pixel-wise prediction tasks, such as semantic segmentation. The segmentation information is obtained by a form of metric learning, where a CNN learns to compute pixel embeddings that reflect whether any pair of pixels is likely to belong to the same region. This information is then used within a larger network, to replace all convolutions with foreground-focused convolutions, where the foreground is determined adaptively at each image point by local embeddings. The resulting network is called a segmentation-aware CNN, because the network can change its behaviour at each image location according to local segmentation cues. The proposed method yields systematic improvements on a standard semantic segmentation benchmark when compared to a strong baseline.

A Convolutional Neural Network-Based Triplet-Mining Metric Learning Schema for Measuring Patient Similarity Addressing Personalized Healthcare

2021 ◽  
Vol 11 (6) ◽  
pp. 1527-1532
Author(s):  
G. Shobana ◽  
S. Shankar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Metric Learning ◽  
Health Care Research ◽  
Personalized Care ◽  
Crucial Information ◽  
Metric Distance ◽  
Fitting In ◽  
Similarity Information

Prediction of the development risk of some diseases is an important area of Health Care Research. When exploring the personalized care of the patients, precise identification and classification of similarity in patients from their past report is an important process. Electronically stored health information EHRs that has been sampled unevenly as well as which has variable appointment durations, is considered to be unsuitable for measuring the similarity among patients directly, as there is no proper representation that are fitting. In addition, a technique is required that is efficient to evaluate similarities in patient. We propose two new similarities learning environments using deep learning that learn simultaneously the representations of the patients as well as measurement of similarity in pairs. A Convolutional Neural Network (CNN) is used to understand EHRs that contains crucial information which are local thereby providing scholastic illumination in the triplet loss otherwise entropy loss. When the training is completed, distances are calculated as well as similarities scores. Using this similarity information, disease predictions along with patient grouping is performed. Experimentally the results gives an idea that CNN can represent the EHR sequences in a better way and the schema offered are more efficient than the modern metric distance learning.

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