Exploration of the Underlying Space in Microscopic Images via Deep Learning for Additively Manufactured Piezoceramics

2021 ◽  
Author(s):  
Wenhua Yang ◽  
Zhuo Wang ◽  
Tiannan Yang ◽  
Li He ◽  
Xuan Song ◽  
...  
Keyword(s):  
Deep Learning ◽  
Microscopic Images ◽  
Underlying Space

scholarly journals Deep learning approach to describe and classify fungi microscopic images

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0234806 ◽  
Author(s):  
Bartosz Zieliński ◽  
Agnieszka Sroka-Oleksiak ◽  
Dawid Rymarczyk ◽  
Adam Piekarczyk ◽  
Monika Brzychczy-Włoch
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Microscopic Images

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
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%.

Using Microscopic Images to Predict Plant Diseases in a Deep Learning Environment

2020 ◽  
pp. 807-813
Author(s):  
Priyadarshini Patil ◽  
Prashant Narayankar ◽  
Deepa Mulimani ◽  
Mayur Patil
Keyword(s):  
Deep Learning ◽  
Learning Environment ◽  
Plant Diseases ◽  
Microscopic Images

Instance Segmentation of 2D Label-Free Microscopic Images using Deep Learning

2021 ◽  
Author(s):  
Basma A. Mohamed ◽  
Lamees N. Mahmoud ◽  
Walid Al-Atabany ◽  
Nancy M. Salem
Keyword(s):  
Deep Learning ◽  
Label Free ◽  
Microscopic Images ◽  
Instance Segmentation

A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images

2021 ◽  
Author(s):  
Ziheng Yang ◽  
Halim Benhabiles ◽  
Karim Hammoudi ◽  
Feryal Windal ◽  
Ruiwen He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Malaria Diagnosis ◽  
Microscopic Images ◽  
Human Malaria

DIAGNOSIS AND CLASSIFICATION ASSISTANCE FROM LYMPHOMA MICROSCOPIC IMAGES USING DEEP LEARNING

2019 ◽  
Vol 37 ◽  
pp. 138-138
Author(s):  
P. Brousset ◽  
C. Syrykh ◽  
A. Abreu ◽  
N. Amara ◽  
C. Laurent
Keyword(s):  
Deep Learning ◽  
Microscopic Images ◽  
Diagnosis And Classification

A deep learning approach for patch-based disease diagnosis from microscopic images

2019 ◽  
pp. 109-127 ◽  
Author(s):  
Anson Simon ◽  
Ravi Vinayakumar ◽  
Viswanathan Sowmya ◽  
Kutti Padannayil Soman ◽  
Ennappadam Anathanarayanan A. Gopalakrishnan
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Microscopic Images

scholarly journals A Deep Learning-Based Method for Automatic Assessment of Stomatal Index in Wheat Microscopic Images of Leaf Epidermis

2021 ◽  
Vol 12 ◽  
Author(s):  
Chuancheng Zhu ◽  
Yusong Hu ◽  
Hude Mao ◽  
Shumin Li ◽  
Fangfang Li ◽  
...  
Keyword(s):  
Deep Learning ◽  
Stomatal Density ◽  
Automatic Measurement ◽  
Epidermal Cells ◽  
Leaf Epidermis ◽  
Stomatal Index ◽  
Automatic Assessment ◽  
Microscopic Images ◽  
Index Measurement ◽  
Diagnostic Characteristics

The stomatal index of the leaf is the ratio of the number of stomata to the total number of stomata and epidermal cells. Comparing with the stomatal density, the stomatal index is relatively constant in environmental conditions and the age of the leaf and, therefore, of diagnostic characteristics for a given genotype or species. Traditional assessment methods involve manual counting of the number of stomata and epidermal cells in microphotographs, which is labor-intensive and time-consuming. Although several automatic measurement algorithms of stomatal density have been proposed, no stomatal index pipelines are currently available. The main aim of this research is to develop an automated stomatal index measurement pipeline. The proposed method employed Faster regions with convolutional neural networks (R-CNN) and U-Net and image-processing techniques to count stomata and epidermal cells, and subsequently calculate the stomatal index. To improve the labeling speed, a semi-automatic strategy was employed for epidermal cell annotation in each micrograph. Benchmarking the pipeline on 1,000 microscopic images of leaf epidermis in the wheat dataset (Triticum aestivum L.), the average counting accuracies of 98.03 and 95.03% for stomata and epidermal cells, respectively, and the final measurement accuracy of the stomatal index of 95.35% was achieved. R2 values between automatic and manual measurement of stomata, epidermal cells, and stomatal index were 0.995, 0.983, and 0.895, respectively. The average running time (ART) for the entire pipeline could be as short as 0.32 s per microphotograph. The proposed pipeline also achieved a good transferability on the other families of the plant using transfer learning, with the mean counting accuracies of 94.36 and 91.13% for stomata and epidermal cells and the stomatal index accuracy of 89.38% in seven families of the plant. The pipeline is an automatic, rapid, and accurate tool for the stomatal index measurement, enabling high-throughput phenotyping, and facilitating further understanding of the stomatal and epidermal development for the plant physiology community. To the best of our knowledge, this is the first deep learning-based microphotograph analysis pipeline for stomatal index assessment.

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

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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