A expert system for stomach cancer images with artificial neural network by using HOG features and linear discriminant analysis: HOG_LDA_ANN

Korkmaz, Sevcan Aytac; Binol, Hamidullah; Akcicek, Aysegul; Korkmaz, Mehmet

doi:10.1109/sisy.2017.8080576

Cited by 22 publications

(13 citation statements)

References 8 publications

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“…Until today, there have been many studies in the field of health [20][21][22][23][24][25][26][27][28][29][30][31][32][33]. However, there are many patients who can not be diagnosed the early cancer.…”

Section: Resultsmentioning

confidence: 99%

Recognition of the İmages with Random Forest Classifier Based on the LPP and LBP Methods

Korkmaz¹

2018

Sakarya University Journal of Science

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İmage cells have taken with Light Microscope help. The local binary pattern (LBP) features have obtained for original images. High-dimensional of these LBP features is reduced to lower-dimensional with Locality Preserving Projections (LPP). These low dimensional data are classified by the Random Forest (RF), Naive Bayes (NB), and Artificial Neural Networks (ANN) classifiers. The classification results are compared with previous studies. The performance achieved with the ANN classifier is higher than the RF and NB classifiers. Moreover, feature vector size used in ANN classifier is a lower than feature vector size used in RF and NB classifiers. The success rates achieved with the ANN, RF, and NB classifiers is respectively 96.29%, 74.44%,and 70.00% according to LPP Method.

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Section: Resultsmentioning

confidence: 99%

Recognition of the İmages with Random Forest Classifier Based on the LPP and LBP Methods

Korkmaz¹

2018

Sakarya University Journal of Science

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“…Predicting the amount of electricity produced in a power plant is very important for today's economy. To date, there are many fi eld work for classifi cation or clustering [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Resultsmentioning

confidence: 99%

Classification by Artificial Neural Network according to the values affecting Electricity Generation

Korkmaz¹

2018

Trends Comput Sci Inf Technol

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Predicting the amount of electricity produced in a power plant is very important for today's economy. Oven Power (MW), Boiler Input Gas Temperature, Superheated Steam Amount, ID-Fan Speed, Feeding Water Tank data affect the electricity production. In this article, Etikrom A.Ş. The electricity production amount to be produced in Elazığ Etikrom A.Ş. was estimated by using the data of Oven Power (MW), Water Inlet Gas Temperature, Steam Vapor Volume, ID-Fan Speed, Feeding Water Tank data. Electricity generation amount is used as verifi cation data. That is, by the k-means clustering method, the electricity generation amount is divided into 3 classes (low, medium, and high). 3621 data including Oven Power (MW), Boiler Input Gas Temperature, Superheated Steam Amount, ID-Fan Speed, and Feeding Water Tank data were used after class 3 separation. With the K-means clustering method, 2742 of these data were clustered as low electricity, 296 as medium electricity and 583 as high electricity. This clustered data was given to the Artifi cal Neural Network classifi er. The success rate obtained as a result of this classifi cation is 85.81%. Classifi ed data were analyzed by ROC curve.

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“…The work in [ 29 ] proposes a HOG-LDA-ANN method for gastric histopathological image classification. HOG features are compared with the GLCM and LBP features.…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

A State‐of‐the‐Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

et al. 2021

BioMed Research International

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Gastric cancer is a common and deadly cancer in the world. The gold standard for the detection of gastric cancer is the histological examination by pathologists, where Gastric Histopathological Image Analysis (GHIA) contributes significant diagnostic information. The histopathological images of gastric cancer contain sufficient characterization information, which plays a crucial role in the diagnosis and treatment of gastric cancer. In order to improve the accuracy and objectivity of GHIA, Computer-Aided Diagnosis (CAD) has been widely used in histological image analysis of gastric cancer. In this review, the CAD technique on pathological images of gastric cancer is summarized. Firstly, the paper summarizes the image preprocessing methods, then introduces the methods of feature extraction, and then generalizes the existing segmentation and classification techniques. Finally, these techniques are systematically introduced and analyzed for the convenience of future researchers.

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A expert system for stomach cancer images with artificial neural network by using HOG features and linear discriminant analysis: HOG_LDA_ANN

Cited by 22 publications

References 8 publications

Recognition of the İmages with Random Forest Classifier Based on the LPP and LBP Methods

Recognition of the İmages with Random Forest Classifier Based on the LPP and LBP Methods

Classification by Artificial Neural Network according to the values affecting Electricity Generation

A State‐of‐the‐Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

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