Evaluation of Morphological Features for Breast Cells Classification Using Neural Networks

2008 ◽  
pp. 1-9
Author(s):  
Harsa Amylia Mat Sakim ◽  
Nuryanti Mohd Salleh ◽  
Mohd Rizal Arshad ◽  
Nor Hayati Othman
Keyword(s):  
Neural Networks ◽  
Morphological Features ◽  
Breast Cells

scholarly journals Predicting the future direction of cell movement with convolutional neural networks

2018 ◽  
Author(s):  
Shori Nishimoto ◽  
Yuta Tokuoka ◽  
Takahiro G Yamada ◽  
Noriko F Hiroi ◽  
Akira Funahashi
Keyword(s):  
Neural Networks ◽  
Cell Fate ◽  
Convolutional Neural Networks ◽  
Cell Shape ◽  
Cell Movement ◽  
Image Features ◽  
Morphological Features ◽  
Current Cell ◽  
The Future ◽  
Future Direction

SummaryImage-based deep learning systems, such as convolutional neural networks (CNNs), have recently been applied to cell classification, producing impressive results; however, application of CNNs has been confined to classification of the current cell state from the image. Here, we focused on cell movement where current and/or past cell shape can influence the future cell fate. We demonstrate that CNNs prospectively predicted the future direction of cell movement with high accuracy from a single image patch of a cell at a certain time. Furthermore, by visualizing the image features that were learned by the CNNs, we could identify morphological features, e.g., the protrusions and trailing edge that have been experimentally reported to determine the direction of cell movement. Our results indicate that CNNs have the potential to predict the future cell fate from current cell shape, and can be used to automatically identify those morphological features that influence future cell fate.

scholarly journals A hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss

2020 ◽  
Vol 6 ◽  
pp. e324
Author(s):  
Tao Wang ◽  
Changhua Lu ◽  
Mei Yang ◽  
Feng Hong ◽  
Chun Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Hybrid Method ◽  
Deep Neural Network ◽  
Morphological Features ◽  
Multilayer Perceptrons ◽  
Medical Monitoring ◽  
Rr Intervals ◽  
Heartbeat Classification

Background Heart arrhythmia, as one of the most important cardiovascular diseases (CVDs), has gained wide attention in the past two decades. The article proposes a hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss. Methods In the method, a convolution neural network is used to extract the morphological features. The reason behind this is that the morphological characteristics of patients have inter-patient variations, which makes it difficult to accurately describe using traditional hand-craft ways. Then the extracted morphological features are combined with the RR intervals features and input into the multilayer perceptron for heartbeat classification. The RR intervals features contain the dynamic information of the heartbeat. Furthermore, considering that the heartbeat classes are imbalanced and would lead to the poor performance of minority classes, a focal loss is introduced to resolve the problem in the article. Results Tested using the MIT-BIH arrhythmia database, our method achieves an overall positive predictive value of 64.68%, sensitivity of 68.55%, f1-score of 66.09%, and accuracy of 96.27%. Compared with existing works, our method significantly improves the performance of heartbeat classification. Conclusions Our method is simple yet effective, which is potentially used for personal automatic heartbeat classification in remote medical monitoring. The source code is provided on https://github.com/JackAndCole/Deep-Neural-Network-For-Heartbeat-Classification.

Montmorillonite Dendrites

1974 ◽  
Vol 32 ◽  
pp. 454-455
Author(s):  
Necip Güven ◽  
Rodney W. Pease
Keyword(s):  
Crystal Growth ◽  
Growth Mechanism ◽  
Growth Front ◽  
Morphological Features ◽  
Dendritic Crystal ◽  
Crystal Growth Mechanism

Morphological features of montmorillonite aggregates in a large number of samples suggest that they may be formed by a dendritic crystal growth mechanism (i.e., tree-like growth by branching of a growth front).

Polymer Morphology Revealed by Staining Techniques

1981 ◽  
Vol 39 ◽  
pp. 340-341
Author(s):  
A. C. Reimschuessel ◽  
V. Kramer
Keyword(s):  
Polymer Melt ◽  
Nylon 6 ◽  
Morphological Features ◽  
Negative Staining ◽  
Polymer Morphology ◽  
Crystallizable Polymer ◽  
Staining Techniques ◽  
Long Chain

Staining techniques can be used for either the identification of different polymers or for the differentiation of specific morphological domains within a given polymer. To reveal morphological features in nylon 6, we choose a technique based upon diffusion of the staining agent into accessible regions of the polymer.When a crystallizable polymer - such as nylon 6 - is cooled from the melt, lamellae form by chainfolding of the crystallizing long chain macromolecules. The regions between adjacent lamellae represent the less ordered amorphous domains into which stain can diffuse. In this process the lamellae will be “outlined” by the dense stain, giving rise to contrast comparable to that obtained by “negative” staining techniques.If the cooling of the polymer melt proceeds relatively slowly - as in molding operations - the lamellae are usually arranged in a radial manner. This morphology is referred to as spherulitic.

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