scholarly journals Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network

2019 ◽  
Vol 9 (16) ◽  
pp. 3362 ◽  
Author(s):  
Shang Shang ◽  
Ling Long ◽  
Sijie Lin ◽  
Fengyu Cong
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Deep Convolutional Neural Network ◽  
Microscopic Image ◽  
Bright Field ◽  
Microscopic Images ◽  
Phenotype Classification ◽  
Microscopic Image Analysis

Zebrafish eggs are widely used in biological experiments to study the environmental and genetic influence on embryo development. Due to the high throughput of microscopic imaging, automated analysis of zebrafish egg microscopic images is highly demanded. However, machine learning algorithms for zebrafish egg image analysis suffer from the problems of small imbalanced training dataset and subtle inter-class differences. In this study, we developed an automated zebrafish egg microscopic image analysis algorithm based on deep convolutional neural network (CNN). To tackle the problem of insufficient training data, the strategies of transfer learning and data augmentation were used. We also adopted the global averaged pooling technique to overcome the subtle phenotype differences between the fertilized and unfertilized eggs. Experimental results of a five-fold cross-validation test showed that the proposed method yielded a mean classification accuracy of 95.0% and a maximum accuracy of 98.8%. The network also demonstrated higher classification accuracy and better convergence performance than conventional CNN methods. This study extends the deep learning technique to zebrafish egg phenotype classification and paves the way for automatic bright-field microscopic image analysis.

Financial Market Prediction Using Recurrence Plot and Convolutional Neural Network

2019 ◽  
Author(s):  
Tameru Hailesilassie
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Financial Market ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Financial Time Series ◽  
Recurrence Plot ◽  
Deep Convolutional Neural Network ◽  
Financial Time ◽  
Insight Into

An application of deep convolutional neural network and recurrence plot for financial market movement prediction is presented. Though it is challenging and subjective to interpret its information, the pattern formed by a recurrence plot provide a useful insight into the dy- namical system. We used a recurrence plot of seven financial time series to train a deep neural network for financial market movement predic- tion. Our approach is tested on our dataset and achieved an average of 53.25% classification accuracy. The result suggests that a well trained deep convolutional neural network can learn a recurrence plot and pre- dict a financial market direction.

scholarly journals Automatic Early Detection of Alzheimer‟s Disease based on 2D-VMD and Deep Convolutional Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 6964-6969
Keyword(s):  
Neural Network ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Early Stage ◽  
Deep Convolutional Neural Network ◽  
Intrinsic Mode Functions ◽  
Automatic Feature Extraction ◽  
Band Limited

In this paper, the classification of normal controls (NC), very mild cognitive impairment and the early stage of Alzheimer’s disease (AD) known as mild cognitive impairment (MCI) from magnetic resonance imaging (MRI) is proposed, based on the two dimensional variational mode decomposition (2D-VMD) and deep convolutional neural network (DCNN). The 2D-VMD is applied to decompose the MRI scans into a discrete number of band limited intrinsic mode functions (BLIMFs). The automatic feature extraction, selection and optimization are performed using the proposed DCNN. The classification accuracy and learning speed of the 2D-VMD-DCNN method are compared with DCNN by taking the MRI data as input. The superior classification accuracy of the proposed 2D-VMD-DCNN method over DCNN method as well as other recently introduced prevalent methods is the major advantage for analyzing the biomedical images in the field of health care

scholarly journals Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network

2019 ◽  
Vol 74 ◽  
pp. 40-50 ◽  
Author(s):  
Yu Wang ◽  
Yating Chen ◽  
Ningning Yang ◽  
Longfei Zheng ◽  
Nilanjan Dey ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Hepatic Granuloma ◽  
Microscopic Images

scholarly journals Tongue Image Quality Assessment Based on Deep Convolutional Neural Network

2020 ◽  
Author(s):  
Tao Jiang ◽  
Xiao-juan Hu ◽  
Xing-hua Yao ◽  
Li-ping Tu ◽  
Jing-bin Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Analysis ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Model Performance ◽  
Poor Quality ◽  
Deep Convolutional Neural Network ◽  
Deep Cnn

Abstract Background: With the wide application of digital tongue diagnosis instrument, massive tongue images will be produced. Adequate image quality is the prerequisite to ensure accurate tongue image analysis. In the process of tongue image collection, improper operation may lead to many poor-quality images (fogging, underexposure, overexposure, blurred focus, wrong tongue posture, etc.), which seriously affect the image processing and the accuracy of image analysis. However traditional pattern recognition is difficult to evaluate the quality of tongue images by extracting features and manual removal of tongue images with bad quality consumes a lot of labor and has a high error rate. In this research, we utilized a deep convolutional neural network to automatically select bad quality tongue images.Methods: The present study was conducted to identify the most appropriate CNN model for Tongue Image Quality Assessment based on deep CNN. The CNN model was evaluated by using Residual neural network and compared with VGGNet and DenseNet. Evaluation metrics such as accuracy, precision, recall, and F1-score were used for CNN model performance.Results: A detection model is established for tongue image quality control based on deep residual network, with an average accuracy of 99.04%, accuracy of 99.05%, recall of 99.04%, and F1-score of 99.05%, which can be used for quality screening of massive tongue images.Conclusions: Our research findings demonstrate various CNN models in the decision-making process for the selection of tongue image quality assessment and prove that applying deep learning methods, specifically deep CNN, to evaluate bad quality tongue images is feasible.

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