An Auto-focus Method for Microscopic Images Based on QSOM Neural Network

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

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

Applied Sciences ◽

10.3390/app9163362 ◽

2019 ◽

Vol 9 (16) ◽

pp. 3362 ◽

Cited By ~ 2

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.

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Identification of Wool and Cashmere Based on Texture Analysis

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.671.385 ◽

2015 ◽

Vol 671 ◽

pp. 385-390 ◽

Cited By ~ 5

Author(s):

Sen Lin Yuan ◽

Kai Lu ◽

Yue Qi Zhong

Keyword(s):

Neural Network ◽

Mathematical Modeling ◽

Texture Analysis ◽

Texture Feature ◽

Digital Camera ◽

Texture Image ◽

Feasible Method ◽

Microscopic Images ◽

Texture Parameters ◽

Network Experiment

In order to separate wool from cashmere efficiently, an identification method based on texture analysis was proposed in this paper. The microscopic images captured by CCD digital camera were preprocessed as the texture image. Improved Tamura texture feature were employed to analyzing the final texture images and to attaining the texture parameters. Through a large number of samples, the mathematical modeling was completed by using neural network. Experiment results indicate that texture analysis can be a feasible method to identify cashmere and wool.

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

E3S Web of Conferences ◽

10.1051/e3sconf/202018502006 ◽

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.

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Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT

Computational and Mathematical Methods in Medicine ◽

10.1155/2022/9934144 ◽

2022 ◽

Vol 2022 ◽

pp. 1-8

Author(s):

Mohammad Manthouri ◽

Zhila Aghajari ◽

Sheida Safary

Keyword(s):

Neural Network ◽

Deep Learning ◽

Blood Cells ◽

Feature Detection ◽

White Blood Cells ◽

Processing System ◽

Health Economy ◽

Microscopic Images ◽

Global Issues ◽

Different Types

Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to detect these diseases is done by analysing the microscopic images of blood cells. Artificial intelligence (AI) techniques are now widely used to detect these blood cells and explore their structures. In recent years, deep learning architectures have been utilized as they are powerful tools for big data analysis. In this work, we are presenting a deep neural network for processing of microscopic images of blood cells. Processing these images is particularly important as white blood cells and their structures are being used to diagnose different diseases. In this research, we design and implement a reliable processing system for blood samples and classify five different types of white blood cells in microscopic images. We use the Gram-Schmidt algorithm for segmentation purposes. For the classification of different types of white blood cells, we combine Scale-Invariant Feature Transform (SIFT) feature detection technique with a deep convolutional neural network. To evaluate our work, we tested our method on LISC and WBCis databases. We achieved 95.84% and 97.33% accuracy of segmentation for these data sets, respectively. Our work illustrates that deep learning models can be promising in designing and developing a reliable system for microscopic image processing.

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