Plant Leaf Segmentation and Phenotypic Analysis Based on Fully Convolutional  Neural Network

HighlightsModify the U-Net segmentation network to reduce the loss of segmentation accuracy.Reducing the number of layers U-net network, modifying the loss function, and the increase in the output layer dropout.It can be well extracted after splitting blade morphological model and color feature.Abstract. From the perspective of computer vision, the shortcut to extract phenotypic information from a single crop in the field is image segmentation. Plant segmentation is affected by the background environment and illumination. Using deep learning technology to combine depth maps with multi-view images can achieve high-throughput image processing. This article proposes an improved U-Net segmentation network, based on small sample data enhancement, and reconstructs the U-Net model by optimizing the model framework, activation function and loss function. It is used to realize automatic segmentation of plant leaf images and extract relevant feature parameters. Experimental results show that the improved model can provide reliable segmentation results under different leaf sizes, different lighting conditions, different backgrounds, and different plant leaves. The pixel-by-pixel segmentation accuracy reaches 0.94. Compared with traditional methods, this network achieves robust and high-throughput image segmentation. This method is expected to provide key technical support and practical tools for top-view image processing, Unmanned Aerial Vehicle phenotype extraction, and phenotype field platforms. Keywords: Deep learning, Full convolution neural network, Image segmentation, Phenotype analysis, U-Net.

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Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

10.26434/chemrxiv.8150666.v1 ◽

2019 ◽

Author(s):

Seoin Back ◽

Junwoong Yoon ◽

Nianhan Tian ◽

Wen Zhong ◽

Kevin Tran ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

High Throughput ◽

High Throughput Screening ◽

Binding Energies ◽

Surface Structures ◽

Voronoi Polyhedra ◽

Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

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Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

Mobile Networks and Applications ◽

10.1007/s11036-020-01703-3 ◽

2021 ◽

Vol 26 (1) ◽

pp. 200-215

Author(s):

Muhammad Alam ◽

Jian-Feng Wang ◽

Cong Guangpei ◽

LV Yunrong ◽

Yuanfang Chen

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Neural Networks ◽

Image Processing ◽

Deep Learning ◽

Semantic Segmentation ◽

Natural Scene ◽

Remote Sensing Images ◽

Advantages And Disadvantages ◽

Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

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A Novel Method of Plant Leaf Disease Detection Based on Deep Learning and Convolutional Neural Network

2021 6th International Conference on Intelligent Computing and Signal Processing (ICSP) ◽

10.1109/icsp51882.2021.9408806 ◽

2021 ◽

Author(s):

Xulang Guan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Disease Detection ◽

Plant Leaf ◽

Leaf Disease ◽

Novel Method

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Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽

10.3390/s21093068 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3068

Author(s):

Soumaya Dghim ◽

Carlos M. Travieso-González ◽

Radim Burget

Keyword(s):

Neural Network ◽

Machine Learning ◽

Image Processing ◽

Deep Learning ◽

The Other ◽

Support Vector ◽

Learning Approaches ◽

Microscopic Images ◽

Trained Neural Network ◽

Nosema Disease

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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HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance

Symmetry ◽

10.3390/sym13112107 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2107

Author(s):

Xin Wei ◽

Huan Wan ◽

Fanghua Ye ◽

Weidong Min

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Loss Function ◽

Network Structure ◽

Medical Diagnosis ◽

Medical Image ◽

Medical Image Segmentation ◽

Training Mode ◽

Cooperative Training

In recent years, medical image segmentation (MIS) has made a huge breakthrough due to the success of deep learning. However, the existing MIS algorithms still suffer from two types of uncertainties: (1) the uncertainty of the plausible segmentation hypotheses and (2) the uncertainty of segmentation performance. These two types of uncertainties affect the effectiveness of the MIS algorithm and then affect the reliability of medical diagnosis. Many studies have been done on the former but ignore the latter. Therefore, we proposed the hierarchical predictable segmentation network (HPS-Net), which consists of a new network structure, a new loss function, and a cooperative training mode. According to our knowledge, HPS-Net is the first network in the MIS area that can generate both the diverse segmentation hypotheses to avoid the uncertainty of the plausible segmentation hypotheses and the measure predictions about these hypotheses to avoid the uncertainty of segmentation performance. Extensive experiments were conducted on the LIDC-IDRI dataset and the ISIC2018 dataset. The results show that HPS-Net has the highest Dice score compared with the benchmark methods, which means it has the best segmentation performance. The results also confirmed that the proposed HPS-Net can effectively predict TNR and TPR.

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Nonlinear Hyperparameter Optimization of a Neural Network in Image Processing for Micromachines

Micromachines ◽

10.3390/mi12121504 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1504

Author(s):

Mingming Shen ◽

Jing Yang ◽

Shaobo Li ◽

Ansi Zhang ◽

Qiang Bai

Keyword(s):

Neural Network ◽

Image Processing ◽

Deep Learning ◽

Network Model ◽

Neural Network Model ◽

Dropout Rate ◽

Adjustment Process ◽

Correlation Model ◽

Mathematical Correlation ◽

Verification Methods

Deep neural networks are widely used in the field of image processing for micromachines, such as in 3D shape detection in microelectronic high-speed dispensing and object detection in microrobots. It is already known that hyperparameters and their interactions impact neural network model performance. Taking advantage of the mathematical correlations between hyperparameters and the corresponding deep learning model to adjust hyperparameters intelligently is the key to obtaining an optimal solution from a deep neural network model. Leveraging these correlations is also significant for unlocking the “black box” of deep learning by revealing the mechanism of its mathematical principle. However, there is no complete system for studying the combination of mathematical derivation and experimental verification methods to quantify the impacts of hyperparameters on the performances of deep learning models. Therefore, in this paper, the authors analyzed the mathematical relationships among four hyperparameters: the learning rate, batch size, dropout rate, and convolution kernel size. A generalized multiparameter mathematical correlation model was also established, which showed that the interaction between these hyperparameters played an important role in the neural network’s performance. Different experiments were verified by running convolutional neural network algorithms to validate the proposal on the MNIST dataset. Notably, this research can help establish a universal multiparameter mathematical correlation model to guide the deep learning parameter adjustment process.

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Sashimi : A toolkit for facilitating high‐throughput organismal image segmentation using deep learning

Methods in Ecology and Evolution ◽

10.1111/2041-210x.13712 ◽

2021 ◽

Cited By ~ 1

Author(s):

Shawn T. Schwartz ◽

Michael E. Alfaro

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

High Throughput

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Peel Learning for Pathway-Related Outcome Prediction

Bioinformatics ◽

10.1093/bioinformatics/btab402 ◽

2021 ◽

Author(s):

Yuantong Li ◽

Fei Wang ◽

Mengying Yan ◽

Edward Cantu ◽

Fan Nils Yang ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Outcome Prediction ◽

Group Structure ◽

Penalized Regression ◽

Small Sample ◽

Feed Forward Neural Network ◽

Gene Expressions ◽

Learning Methods ◽

Post Surgery

Abstract Motivation Traditional regression models are limited in outcome prediction due to their parametric nature. Current deep learning methods allow for various effects and interactions and have shown improved performance, but they typically need to be trained on a large amount of data to obtain reliable results. Gene expression studies often have small sample sizes but high dimensional correlated predictors so that traditional deep learning methods are not readily applicable. Results In this paper, we proposed peel learning, a novel neural network that incorporates the prior relationship among genes. In each layer of learning, overall structure is peeled into multiple local substructures. Within the substructure, dependency among variables is reduced through linear projections. The overall structure is gradually simplified over layers and weight parameters are optimized through a revised backpropagation. We applied PL to a small lung transplantation study to predict recipients’ post-surgery primary graft dysfunction using donors’ gene expressions within several immunology pathways, where PL showed improved prediction accuracy compared to conventional penalized regression, classification trees, feed-forward neural network, and a neural network assuming prior network structure. Through simulation studies, we also demonstrated the advantage of adding specific structure among predictor variables in neural network, over no or uniform group structure, which is more favorable in smaller studies. The empirical evidence is consistent with our theoretical proof of improved upper bound of PL’s complexity over ordinary neural networks. Availability and Implementation PL algorithm was implemented in Python and the open-source code and instruction will be available at https://github.com/Likelyt/Peel-Learning

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Bio-Medical Image Processing

Examining Fractal Image Processing and Analysis - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-0066-8.ch007 ◽

2020 ◽

pp. 158-169

Author(s):

Rasmita Lenka ◽

Koustav Dutta ◽

Ashimananda Khandual ◽

Soumya Ranjan Nayak

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Deep Learning ◽

Early Detection ◽

Digital Image Processing ◽

Medical Science ◽

Medical Image Processing ◽

The World ◽

Medical Reports

The chapter focuses on application of digital image processing and deep learning for analyzing the occurrence of malaria from the medical reports. This approach is helpful in quick identification of the disease from the preliminary tests which are carried out in a person affected by malaria. The combination of deep learning has made the process much advanced as the convolutional neural network is able to gain deeper insights from the medical images of the person. Since traditional methods are not able to detect malaria properly and quickly, by means of convolutional neural networks, the early detection of malaria has been possible, and thus, this process will open a new door in the world of medical science.

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