Suitability Check of Haralick Features for Classification of Different Types of Defects in Silk Fabric Material

2020 ◽  
Vol 7 (8) ◽  
Keyword(s):  
Silk Fabric ◽  
Different Types ◽  
Haralick Features ◽  
Fabric Material

scholarly journals Generic Wrapper Based Model using Haralick Features for Silk Fabric Defect Classification

2021 ◽  
Vol 23 (11) ◽  
pp. 867-878
Author(s):  
Ms. Shweta Loonkar ◽  
◽  
Dhirendra S. Mishra ◽  
Surya S. Durbha ◽  
◽  
...  
Keyword(s):  
Defect Detection ◽  
Control Unit ◽  
Gabor Transform ◽  
Defect Classification ◽  
Silk Fabric ◽  
Fabric Defect ◽  
Segmented Images ◽  
Wrapper Method ◽  
Haralick Features

Quality control unit of fabric industry looks for the effective defect detection methodology. The research is required to be done in this area to develop such solution. Various models based on combination of suitable feature extraction, selection and classification approaches need to be experimented out for the same. This paper attempts to experiment and provide such models mainly based on generic wrapper based selection approaches. Widely used broader range of Haralick features are prominently used for detection and classification of defects in this research. It also attempts to identify the suitability of these features based on segmented images provided as an input. The research has been carried on TILDA Dataset consisting of 800 Silk Fabric Images with eight different defects present on it and each carrying 100 images per defect. Models generated using generic wrapper based approach has also been compared with the Gabor Transforms. Then identification of suitable Haralick Features for particular type of defects has been carried out. In this 68% classification accuracy has been achieved using generic wrapper method and 40 % accuracy has been achieved using Gabor Transform with respect to fourteen Haralick Features and seven types of defects.

Dual stain kit for the microscopic gram classification of ocular infection bacteria

1993 ◽  
Vol 51 ◽  
pp. 248-249
Author(s):  
Jacob S. Hanker ◽  
Dale N. Holdren ◽  
Kenneth L. Cohen ◽  
Beverly L. Giammara
Keyword(s):  
Contact Lenses ◽  
Ocular Infection ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Gram Staining ◽  
Ocular Infections ◽  
Different Types ◽  
Culture Positive ◽  
Increased Susceptibility

Keratitis and conjunctivitis (infections of the cornea or conjunctiva) are ocular infections caused by various bacteria, fungi, viruses or parasites; bacteria, however, are usually prominent. Systemic conditions such as alcoholism, diabetes, debilitating disease, AIDS and immunosuppressive therapy can lead to increased susceptibility but trauma and contact lens use are very important factors. Gram-negative bacteria are most frequently cultured in these situations and Pseudomonas aeruginosa is most usually isolated from culture-positive ulcers of patients using contact lenses. Smears for staining can be obtained with a special swab or spatula and Gram staining frequently guides choice of a therapeutic rinse prior to the report of the culture results upon which specific antibiotic therapy is based. In some cases staining of the direct smear may be diagnostic in situations where the culture will not grow. In these cases different types of stains occasionally assist in guiding therapy.

Explanatory Power for Medical Expert Systems: Studies in the Representation of Causal Relationships for Clinical Consultations

1982 ◽  
Vol 21 (03) ◽  
pp. 127-136 ◽  
Author(s):  
J. W. Wallis ◽  
E. H. Shortliffe
Keyword(s):  
Explanatory Power ◽  
Prototype System ◽  
Inference Rules ◽  
Medical Expert ◽  
Comprehensive Knowledge ◽  
Different Types ◽  
Medical Expert Systems ◽  
Explanation System ◽  
Systems Studies

This paper reports on experiments designed to identify and implement mechanisms for enhancing the explanation capabilities of reasoning programs for medical consultation. The goals of an explanation system are discussed, as is the additional knowledge needed to meet these goals in a medical domain. We have focussed on the generation of explanations that are appropriate for different types of system users. This task requires a knowledge of what is complex and what is important; it is further strengthened by a classification of the associations or causal mechanisms inherent in the inference rules. A causal representation can also be used to aid in refining a comprehensive knowledge base so that the reasoning and explanations are more adequate. We describe a prototype system which reasons from causal inference rules and generates explanations that are appropriate for the user.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Preventing corneal blindness caused by keratitis using artificial intelligence

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhongwen Li ◽  
Jiewei Jiang ◽  
Kuan Chen ◽  
Qianqian Chen ◽  
Qinxiang Zheng ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Vision Loss ◽  
Learning System ◽  
Automated Classification ◽  
Slit Lamp ◽  
Resource Limited ◽  
The World ◽  
Different Types ◽  
Corneal Blindness

AbstractKeratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.

NEURAL CLASSIFICATION OF MASS ABNORMALITIES WITH DIFFERENT TYPES OF FEATURES IN DIGITAL MAMMOGRAPHY

2006 ◽  
Vol 06 (01) ◽  
pp. 61-75 ◽  
Author(s):  
R. PANCHAL ◽  
B. VERMA
Keyword(s):  
Breast Cancer ◽  
Research Work ◽  
Breast Cancer Diagnosis ◽  
Classification Rate ◽  
Test Dataset ◽  
Benchmark Database ◽  
Different Types ◽  
Breast Tissues ◽  
Mass Type

Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BI-RADS features, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

scholarly journals Space Precession Target Classification Based on Radar High-Resolution Range Profiles

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yizhe Wang ◽  
Cunqian Feng ◽  
Yongshun Zhang ◽  
Sisan He
Keyword(s):  
Parameter Extraction ◽  
Classification Performance ◽  
Support Vector ◽  
Electromagnetic Data ◽  
Feature Extractor ◽  
Different Types ◽  
Radar Echo ◽  
High Level ◽  
Cone Target

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

Lexicographer’s Lacunas or How to Deal with Missing Representative Dictionary Forms on the Example of Czech

2019 ◽  
Author(s):  
Dominika Kováříková ◽  
Michal Škrabal ◽  
Václav Cvrček ◽  
Lucie Lukešová ◽  
Jiří Milička
Keyword(s):  
Grammatical Category ◽  
Manual Analysis ◽  
Different Types ◽  
Word Forms ◽  
Frequency Threshold

Abstract When compiling a list of headwords, every lexicographer comes across words with an unattested representative dictionary form in the data. This study focuses on how to distinguish between the cases when this form is missing due to a lack of data and when there are some systemic or linguistic reasons. We have formulated lexicographic recommendations for different types of such ‘lacunas’ based on our research carried out on Czech written corpora. As a prerequisite, we calculated a frequency threshold to find words that should have the representative form attested in the data. Based on a manual analysis of 2,700 nouns, adjectives and verbs that do not, we drew up a classification of lacunas. The reasons for a missing dictionary form are often associated with limited collocability and non-preference for the representative grammatical category. Findings on unattested word forms also have significant implications for language potentiality.

scholarly journals ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Eva Volna ◽  
Martin Kotyrba ◽  
Hashim Habiballa
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Ecg Signals ◽  
Detection Systems ◽  
Language Interpretation ◽  
Different Types ◽  
Time Courses ◽  
Natural Language Interpretation ◽  
Unique Capability

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

A radiopathological classification of dural tail sign of meningiomas

2012 ◽  
Vol 117 (4) ◽  
pp. 645-653 ◽  
Author(s):  
Song-tao Qi ◽  
Yi Liu ◽  
Jun Pan ◽  
Silky Chotai ◽  
Lu-xiong Fang
Keyword(s):  
Tumor Cells ◽  
Histopathological Examination ◽  
Who Grade ◽  
Simpson Grade ◽  
Mri Studies ◽  
Dural Tail Sign ◽  
Significant Difference ◽  
Different Types ◽  
Dural Tail

Object The completeness of meningioma resection depends on the resection of dura mater invaded by the tumor. The pathological changes of the dura around the tumor can be interpreted by evaluating the dural tail sign (DTS) on MRI studies. The goal of this study was to clarify the pathological characteristics of the DTSs, propose a classification based on the histopathological and radiological correlation, and identify the invasive range of tumor cells in different types of DTS. Methods The authors retrospectively reviewed 179 patients with convexity meningiomas who underwent Simpson Grade I resection. All patients underwent an enhanced MRI examination preoperatively. The convexity meningiomas were dichotomized into various subtypes in accordance with the 2007 WHO classification of tumors of the CNS, and the DTS was identified based on the Goldsher criteria. The range of resection of the involved dura was 3 cm from the base of the tumor, which corresponded with the length of DTS on MRI studies. Histopathological examination of dura at 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 cm from the base of the tumor was conducted, and the findings were correlated with the preoperative MRI appearance of the DTS. Results A total of 154 (86%) of 179 convexity meningiomas were classified into WHO Grade I subtype, including transitional (44 [28.6%] of 154), meningothelial (36 [23.4%] of 154), fibrous (23 [14.9%] of 154), psammomatous (22 [14.3%] of 154), secretory (10 [6.5%] of 154), and angiomatous (19 [12.3%] of 154). The other 25 (14%) were non–Grade I (WHO) tumors, including atypical (12 [48%] of 25), anaplastic (5 [20%] of 25), and papillary (8 [32%] of 25). The DTS was classified into 5 types: smooth (16 [8.9%] of 179), nodular (36 [20.1%] of 179), mixed (57 [31.8%] of 179), symmetrical multipolar (15 [8.4%] of 179), and asymmetrical multipolar (55 [30.7%] of 179). There was a significant difference in distribution of DTS type between Grade I and non–Grade I tumors (p = 0.004), whereas the difference was not significant among Grade I tumors (0.841) or among non–Grade I tumors (p = 0.818). All smooth-type DTSs were encountered in Grade I tumors, and the mixed DTS (52 [33.8%] of 154) was the most common type in these tumors. Nodular-type DTS was more commonly seen in non–Grade I tumors (12 [48%] of 25). Tumor invasion was found in 88.3% (158 of 179) of convexity meningiomas, of which the range of invasion in 82.3% (130 of 158) was within 2 cm and that in 94.9% (150 of 158) was within 2.5 cm. The incidence of invasion and the range invaded by tumor cells varied in different types of DTS, and differences were statistically significant (p < 0.001). Conclusions Nodular-type DTS on MRI studies might be associated with non–Grade I tumors. The range of dural resection for convexity meningiomas should be 2.5 cm from the tumor base, and if this extent of resection is not feasible, the type of DTS should be considered. However, for skull base meningiomas, in which mostly Simpson Grade II resection is achieved, the use of this classification should be further validated. The classification of DTS enables the surgeon to predict preoperatively and then to achieve the optimal range of dural resection that might significantly reduce the recurrence rate of meningiomas.

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