SHAPE AND TEXTURE INDEXES APPLICATION TO CELL NUCLEI CLASSIFICATION

2013 ◽  
Vol 27 (01) ◽  
pp. 1357002 ◽  
Author(s):  
GUILLAUME THIBAULT ◽  
BERNARD FERTIL ◽  
CLAIRE NAVARRO ◽  
SANDRINE PEREIRA ◽  
PIERRE CAU ◽  
...  
Keyword(s):  
Large Range ◽  
New Method ◽  
Classification Model ◽  
Classification Method ◽  
Cell Nuclei ◽  
Characterization Techniques ◽  
A Cell ◽  
Texture Characterization

This paper describes the sequence of construction of a cell nuclei classification model by the analysis, the characterization and the classification of shape and texture. We describe first the elaboration of dedicated shape indexes and second the construction of the associated classification submodel. Then we present a new method of texture characterization, based on the construction and the analysis of statistical matrices encoding the texture. The various characterization techniques developed in this paper are systematically compared to previous approaches. In particular, we paid special attention to the results obtained by a versatile classification method using a large range of descriptors dedicated to the characterization of shapes and textures. Finally, the last classifier built with our methods achieved 88% of classification out of the 94% possible.

scholarly journals Research on Automatic Classification Method of Ethnic Music Emotion Based on Machine Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Zijin Wu
Keyword(s):  
Folk Music ◽  
Classification Accuracy ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Method ◽  
Support Vector ◽  
Ethnic Music ◽  
Huge Impact ◽  
Speed Up

With the development of the country’s economy, there is a flourishing situation in the field of culture and art. However, the diversification of artistic expressions has not brought development to folk music. On the contrary, it brought a huge impact, and some national music even fell into the dilemma of being lost. This article is mainly aimed at the recognition and classification of folk music emotions and finds the model that can make the classification accuracy rate as high as possible. The classification model used in this article is mainly after determining the use of Support Vector Machine (SVM) classification method, a variety of attempts have been made to feature extraction, and good results have been achieved. Explore the Deep Belief Network (DBN) pretraining and reverse fine-tuning process, using DBN to learn the fusion characteristics of music. According to the abstract characteristics learned by them, the recognition and classification of folk music emotions are carried out. The DBN is improved by adding “Dropout” to each Restricted Boltzmann Machine (RBM) and adjusting the increase standard of weight and bias. The improved network can avoid the overfitting problem and speed up the training of the network. Through experiments, it is found that using the fusion features proposed in this paper, through classification, the classification accuracy has been improved.

scholarly journals Classification Method of Plug Seedlings Based on Transfer Learning

2019 ◽  
Vol 9 (13) ◽  
pp. 2725 ◽  
Author(s):  
Zhang Xiao ◽  
Yu Tan ◽  
Xingxing Liu ◽  
Shenghui Yang
Keyword(s):  
Transfer Learning ◽  
Cumulative Distribution ◽  
Classification Model ◽  
Classification Method ◽  
Learning Method ◽  
Important Work ◽  
Training Time ◽  
Average Accuracy ◽  
Peak Points

The classification of plug seedlings is important work in the replanting process. This paper proposed a classification method for plug seedlings based on transfer learning. Firstly, by extracting and graying the interest region of the original image acquired, a regional grayscale cumulative distribution curve is obtained. Calculating the number of peak points of the curve to identify the plug tray specification is then done. Secondly, the transfer learning method based on convolutional neural network is used to construct the classification model of plug seedlings. According to the growth characteristics of the seedlings, 2286 seedlings samples were collected to train the model at the two-leaf and one-heart stages. Finally, the image of the interest region is divided into cell images according to the specification of the plug tray, and the cell images are put into the classification model, thereby classifying the qualified seedling, the unqualified seedling and the lack of seedling. After testing, the identification method of the tray specification has an average accuracy of 100% for the three specifications (50 cells, 72 cells, 105 cells) of the 20-day and 25-day pepper seedlings. Seedling classification models based on the transfer learning method of four different convolutional neural networks (Alexnet, Inception-v3, Resnet-18, VGG16) are constructed and tested. The classification accuracy of the VGG16-based classification model is the best, which is 95.50%, the Alexnet-based classification model has the shortest training time, which is 6 min and 8 s. This research has certain theoretical reference significance for intelligent replanting classification work.

scholarly journals Design of Accurate Classification of COVID-19 Disease in X-Ray Images Using Deep Learning Approach

2021 ◽  
Vol 2 (2) ◽  
pp. 132-148
Author(s):  
Joy Iong-Zong Chen
Keyword(s):  
Transfer Learning ◽  
Research Work ◽  
Well Being ◽  
Classification Model ◽  
Classification Method ◽  
World Health ◽  
Detection Methods ◽  
X Rays ◽  
X Ray

COVID-19 appears to be having a devastating influence on world health and well-being. Moreover, the COVID-19 confirmed cases have recently increased to over 10 million worldwide. As the number of verified cases increase, it is more important to monitor and classify healthy and infected people in a timely and accurate manner. Many existing detection methods have failed to detect viral patterns. Henceforth, by using COVID-19 thoracic x-rays and the histogram-oriented gradients (HOG) feature extraction methodology; this research work has created an accurate classification method for performing a reliable detection of COVID-19 viral patterns. Further, the proposed classification model provides good results by leveraging accurate classification of COVID-19 disease based on the medical images. Besides, the performance of our proposed CNN classification method for medical imaging has been assessed based on different edge-based neural networks. Whenever there is an increasing number of a class in the training network, the accuracy of tertiary classification with CNN will be decreasing. Moreover, the analysis of 10 fold cross-validation with confusion metrics can also take place in our research work to detect various diseases caused due to lung infection such as Pneumonia corona virus-positive or negative. The proposed CNN model has been trained and tested with a public X-ray dataset, which is recently published for tertiary and normal classification purposes. For the instance transfer learning, the proposed model has achieved 85% accuracy of tertiary classification that includes normal, COVID-19 positive and Pneumonia. The proposed algorithm obtains good classification accuracy during binary classification procedure integrated with the transfer learning method.

scholarly journals The Classification of Documents in Malay and Indonesian Using the Naive Bayesian Method Uses Words and Phrases as a Training Set

MENDEL ◽  
2020 ◽  
Vol 26 (2) ◽  
pp. 23-28
Author(s):  
Marvin Chandra Wijaya
Keyword(s):  
Classification Accuracy ◽  
Bayesian Method ◽  
New Method ◽  
Classification Method ◽  
Training Set ◽  
Naive Bayesian ◽  
Naïve Bayesian

Malay Language and Indonesian Language are two closely related languages, sharing a lot in common in the meanings of words and grammar. Classifying the two languages automatically using a tool is a challenge because the two languages are very similar. The classification method that is widely used today is the Naive Bayesian method. This method needs to be implemented in a particular way to increase the level of classification accuracy. In this study, a new method was used, by using a training set in the form of words and phrases instead of just using a training set in the form of words only. With this method, the level of classification accuracy of the two languages is increased.

scholarly journals Detection and Classification of Interacting Persons

2010 ◽  
pp. 213-227
Author(s):  
Scott Blunsden ◽  
Robert Fisher
Keyword(s):  
State Of The Art ◽  
New Method ◽  
Classification Method ◽  
Previous State ◽  
New Feature ◽  
Better Than

This chapter presents a way to classify interactions between people. Examples of the interactions we investigate are: people meeting one another, walking together, and fighting. A new feature set is proposed along with a corresponding classification method. Results are presented which show the new method performing significantly better than the previous state of the art method as proposed by Oliver et al. (2000).

scholarly journals Analysis of the foetal heart rate in cardiotocographic recordings through a progressive characterization of decelerations

2017 ◽  
Vol 3 (2) ◽  
pp. 423-427 ◽  
Author(s):  
Patricio Fuentealba ◽  
Alfredo Illanes ◽  
Frank Ortmeier
Keyword(s):  
Heart Rate ◽  
Median Filter ◽  
New Method ◽  
Foetal Heart Rate ◽  
Foetal Heart

AbstractThe main purpose of this work is to propose a new method for characterization and visualization of FHR deceleration episodes in terms of their depth, length and location. This is performed through the estimation of a progressive baseline computed using a median filter allowing to identify and track the evolution of decelerations in cardiotocographic CTG recordings. The proposed method has been analysed using three representative cases of normal and pathological CTG recordings extracted from the CTU-UHB database freely available on the PhysioNet Website. Results show that both the progressive baseline and the parameterized deceleration episodes can describe different time-variant behaviour, whose characteristics and progression can help the observer to discriminate between normal and pathological FHR signal patterns. This opens perspectives for classification of non-reassuring CTG recordings as a sign of foetal acidemia.

scholarly journals Characterization of an rRNA Operon (rrnB) of Mycobacterium fortuitum and Other Mycobacterial Species: Implications for the Classification of Mycobacteria

2002 ◽  
Vol 184 (4) ◽  
pp. 1078-1088 ◽  
Author(s):  
M. C. Menendez ◽  
M. J. Garcia ◽  
M. C. Navarro ◽  
J. A. Gonzalez-y-Merchand ◽  
S. Rivera-Gutierrez ◽  
...  
Keyword(s):  
16S Rrna ◽  
Rrna Synthesis ◽  
Rrna Gene ◽  
Mycobacterium Fortuitum ◽  
Mycobacterial Species ◽  
A Cell ◽  
Rrna Sequences ◽  
Similar Organization

ABSTRACT Mycobacteria are thought to have either one or two rRNA operons per genome. All mycobacteria investigated to date have an operon, designated rrnA, located downstream from the murA gene. We report that Mycobacteriun fortuitum has a second rrn operon, designated rrnB, which is located downstream from the tyrS gene; tyrS is very close to the 3" end of a gene (3-mag) coding for 3-methylpurine-DNA-glycosylase. The second rrn operon of Mycobacterium smegmatis was shown to have a similar organization, namely, 5" 3-mag-tyrS-rrnB 3". The rrnB operon of M. fortuitum was found to have a single dedicated promoter. During exponential growth in a rich medium, the rrnB and rrnA operons were the major and minor contributors, respectively, to pre-rRNA synthesis. Genomic DNA was isolated from eight other fast-growing mycobacterial species. Samples were investigated by Southern blot analysis using probes for murA, tyrS, and 16S rRNA sequences. The results revealed that both rrnA and rrnB operons were present in each species. The results form the basis for a proposed new scheme for the classification of mycobacteria. The approach, which is phylogenetic in concept, is based on particular properties of the rrn operons of a cell, namely, the number per genome and a feature of 16S rRNA gene sequences.

dC/dV and CV Characterization of Gate Resistance Defects in eDRAM Circuits

2013 ◽  
Author(s):  
Sweta Pendyala ◽  
Dave Albert ◽  
Katherine Hawkins ◽  
Michael Tenney
Keyword(s):  
Deep Submicron ◽  
Work Flow ◽  
Localization Technique ◽  
Characterization Techniques ◽  
Capacitance Voltage ◽  
Gate Resistance

Abstract Resistive gate defects are unusual and difficult to detect with conventional techniques [1] especially on advanced devices manufactured with deep submicron SOI technologies. An advanced localization technique such as Scanning Capacitance Imaging is essential for localizing these defects, which can be followed by DC probing, dC/dV, CV (Capacitance-Voltage) measurements to completely characterize the defect. This paper presents a case study demonstrating this work flow of characterization techniques.

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