scholarly journals Leukocyte recognition algorithm in leucorrhea microscopic images based on ResNet-34 neural network

2020 ◽  
Vol 185 ◽  
pp. 02006
Author(s):  
Xiuchao Wan ◽  
Zhiyong Wang ◽  
Guanghui Kong ◽  
Fengjun Xue
Keyword(s):  
Neural Network ◽  
Effective Means ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Microscopic Image ◽  
Microscopic Images ◽  
Canny Edge ◽  
Important Sign ◽  
Low Efficiency

Automatic leucocyte recognition for leukorrhea microscopic images is a digital image processing technology in the field of machine learning. The existence and quantity of leukocytes in leucorrhea microscopic image is an important sign and basis to judge the inflammation of vagina or cervix. Therefore, the recognition and count of leucocyte is an effective means to evaluate the condition of the disease. To solve the problem of low efficiency of leucocyte recognition in traditional artificial microscopy, this paper proposes an automatic recognition algorithm based on ResNet-34 neural network. Firstly, Canny edge detection algorithm based on genetic algorithm is used to extract the foreground target in the leucorrhea microscopic image. Secondly, the leucocyte target is selected according to the connected region and boundary rectangle parameters of the foreground target. Finally, ResNet-34 neural network is applied for the classification of leukocytes. Experiments show that the recognition accuracy of leukocytes in leucorrhea microscopic image is 92.8%, and the recall is 97.1%, which is higher and better than other methods.

Effective Classification of Melting Curve in Real-Time PCR based on Dynamic Filter-based Convolutional Neural Network

2021 ◽  
Vol 16 ◽  
Author(s):  
Di Gai ◽  
Xuanjing Shen ◽  
Haipeng Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Melting Curve ◽  
Classification Model ◽  
Proposed Model ◽  
Unbalanced Dataset ◽  
Manual Selection ◽  
Low Efficiency ◽  
Dynamic Filter

Background: The effective classification of the melting curve is conducive to measure the specificity of the amplified products and the influence of invalid data on subsequent experiments is excluded. Objective: In this paper, a convolutional neural network (CNN) classification model based on dynamic filter is proposed, which can categorize the number of peaks in the melting curve image and distinguish the pollution data represented by the noise peaks. Method: The main advantage of the proposed model is that it adopts the filter which changes with the input and uses the dynamic filter to capture more information in the image, making the network learning more accurate. In addition, the residual module is used to extract the characteristics of the melting curve, and the pooling operation is replaced with an atrous convolution to prevent the loss of context information. Result: In order to train the proposed model, a novel melting curve dataset is created, which includes a balanced dataset and an unbalanced dataset. The proposed method uses six classification-based assessment criteria to compare with seven representative methods based on deep learning. Experimental results show that proposed method is not only markedly outperforms the other state-of-the-art methods in accuracy, but also has much less running time. Conclusion: It evidently proves that the proposed method is suitable for judging the specificity of amplification products according to the melting curve. Simultaneously, it overcomes the difficulties of manual selection with low efficiency and artificial bias.

scholarly journals I3D-Shufflenet Based Human Action Recognition

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 301
Author(s):  
Guocheng Liu ◽  
Caixia Zhang ◽  
Qingyang Xu ◽  
Ruoshi Cheng ◽  
Yong Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Convolution Kernel ◽  
Histogram Of Oriented Gradients ◽  
Temporal Features ◽  
Convolution Kernels

In view of difficulty in application of optical flow based human action recognition due to large amount of calculation, a human action recognition algorithm I3D-shufflenet model is proposed combining the advantages of I3D neural network and lightweight model shufflenet. The 5 × 5 convolution kernel of I3D is replaced by a double 3 × 3 convolution kernels, which reduces the amount of calculations. The shuffle layer is adopted to achieve feature exchange. The recognition and classification of human action is performed based on trained I3D-shufflenet model. The experimental results show that the shuffle layer improves the composition of features in each channel which can promote the utilization of useful information. The Histogram of Oriented Gradients (HOG) spatial-temporal features of the object are extracted for training, which can significantly improve the ability of human action expression and reduce the calculation of feature extraction. The I3D-shufflenet is testified on the UCF101 dataset, and compared with other models. The final result shows that the I3D-shufflenet has higher accuracy than the original I3D with an accuracy of 96.4%.

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.

MCE-BASED FACE RECOGNITION

2001 ◽  
Vol 15 (08) ◽  
pp. 1311-1327 ◽  
Author(s):  
LIANG-HUA CHEN ◽  
SHAO-HUA DENG ◽  
HONG-YUAN LIAO
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Face Detection ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Classification Error ◽  
Complex Scenes ◽  
Minimum Classification Error ◽  
Complete Procedure ◽  
Human Faces

This paper proposes a complete procedure for the extraction and recognition of human faces in complex scenes. The morphology-based face detection algorithm can locate multiple faces oriented in any direction. The recognition algorithm is based on the minimum classification error (MCE) criterion. In our work, the minimum classification error formulation is incorporated into a multilayer perceptron neural network. Experimental results show that our system is robust to noisy images and complex background.

scholarly journals Edge Detecting Method for Microscopic Image of Cotton Fiber Cross-Section Using RCF Deep Neural Network

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 196
Author(s):  
Defeng He ◽  
Quande Wang
Keyword(s):  
Neural Network ◽  
Receptive Field ◽  
Cross Section ◽  
Cotton Fiber ◽  
Detection Algorithm ◽  
Microscopic Image ◽  
Edge Information ◽  
Fiber Cross Section ◽  
Edge Detecting ◽  
Supervision Model

Currently, analyzing the microscopic image of cotton fiber cross-section is the most accurate and effective way to measure its grade of maturity and then evaluate the quality of cotton samples. However, existing methods cannot extract the edge of the cross-section intact, which will affect the measurement accuracy of maturity grade. In this paper, a new edge detection algorithm that is based on the RCF convolutional neural network (CNN) is proposed. For the microscopic image dataset of the cotton fiber cross-section constructed in this paper, the original RCF was firstly used to extract the edge of the cotton fiber cross-section in the image. After analyzing the output images of RCF in each convolution stage, the following two conclusions are drawn: (1) the shallow layers contain a lot of important edge information of the cotton fiber cross-section; (2) because the size of the cotton fiber cross-section in the image is relatively small and the receptive field of the convolutional layer gradually increases with the deepening of the number of layers, the edge information detected by the deeper layers becomes increasingly coarse. In view of the above two points, the following improvements are proposed in this paper: (1) modify the network supervision model and loss calculation structure; (2) the dilated convolution in the deeper layers is removed; therefore, the receptive field in the deeper layers is reduced to adapt to the detection of small objects. The experimental results show that the proposed method can effectively improve the accuracy of edge extraction of cotton fiber cross-section.

scholarly journals Object Detection and Classification of Metal Polishing Shaft Surface Defects Based on Convolutional Neural Network Deep Learning

2019 ◽  
Vol 10 (1) ◽  
pp. 87 ◽  
Author(s):  
Qingsheng Jiang ◽  
Dapeng Tan ◽  
Yanbiao Li ◽  
Shiming Ji ◽  
Chaopeng Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Surface Defects ◽  
Engineering Application ◽  
Light Surface ◽  
Low Efficiency ◽  
Shaft Surface

Defective shafts need to be classified because some defective shafts can be reworked to avoid replacement costs. Therefore, the detection and classification of shaft surface defects has important engineering application value. However, in the factory, shaft surface defect inspection and classification are done manually, with low efficiency and reliability. In this paper, a deep learning method based on convolutional neural network feature extraction is used to realize the object detection and classification of metal shaft surface defects. Through image segmentation, the system methods setting of a Fast-R-CNN object detection framework and parameter optimization settings are implemented to realize the classification of 16,384 × 4096 large image little objects. The experiment proves that the method can be applied in practical production and can also be extended to other fields of large image micro-fine defects with a high light surface. In addition, this paper proposes a method to increase the proportion of positive samples by multiple settings of IOU values and discusses the limitations of the system for defect detection.

scholarly journals Research on Pepper External Quality Detection Based on Transfer Learning Integrated with Convolutional Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5305
Author(s):  
Rui Ren ◽  
Shujuan Zhang ◽  
Haixia Sun ◽  
Tingyao Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Contrast Ratio ◽  
Classification Model ◽  
Stable Convergence ◽  
External Quality ◽  
Quality Detection ◽  
Low Efficiency

A pepper quality detection and classification model based on transfer learning combined with convolutional neural network is proposed as a solution for low efficiency of manual pepper sorting at the current stage. The pepper dataset was amplified with data pre-processing methods including rotation, luminance switch, and contrast ratio switch. To improve training speed and precision, a network model was optimized with a fine-tuned VGG 16 model in this research, transfer learning was applied after parameter optimization, and comparative analysis was performed by combining ResNet50, MobileNet V2, and GoogLeNet models. It turned out that the VGG 16 model output anticipation precision was 98.14%, and the prediction loss rate was 0.0669 when the dropout was settled as 0.3, learning rate settled as 0.000001, batch normalization added, and ReLU as activation function. Comparing with other finetune models and network models, this model was of better anticipation performance, as well as faster and more stable convergence rate, which embodied the best performance. Considering the basis of transfer learning and integration with strong generalization and fitting capacity of the VGG 16 finetune model, it is feasible to apply this model to the external quality classification of pepper, thus offering technical reference for further realizing the automatic classification of pepper quality.

An Intelligent Recognition Algorithm on Traffic Safety States

2013 ◽  
Vol 433-435 ◽  
pp. 1388-1391 ◽  
Author(s):  
Wei Zhi Wang ◽  
Bing Han Liu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Traffic Safety ◽  
Video Processing ◽  
High Accuracy ◽  
Recognition Algorithm ◽  
Video Images ◽  
Network Methods ◽  
Intelligent Recognition

Traffic safety states can be divided into safe and dangerous according to the attributes of video images of traffic safety states. We propose a synergic neural network recognition model based on prototype pattern by analyzing various methods on intelligent video processing. Our proposed method realizes real time classification of traffic safety states with high accuracy of traffic safety states recognition. The experimental results validate that the accuracy of classification of proposed method arrives at 87.5%, increased by 16.2% compared to traditional neural network methods.

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