Recognition of the stomach cancer images with probabilistic HOG feature vector histograms by using HOG features

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

doi:10.1109/sisy.2017.8080578

Cited by 45 publications

(22 citation statements)

References 5 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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“…erefore, HOG is used to extract information of the target edge in a local region of the original image, and this has become an important means of referencing certain target shapes. [24] e description of HOG feature extraction is as follows.…”

Section: Histogram Of Oriented Gradients (Hog)mentioning

confidence: 99%

Self-Interacting Proteins Prediction from PSSM Based on Evolutionary Information

Wang

et al. 2021

Scientific Programming

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Self-interacting proteins (SIPs) play an influential role in regulating cell structure and function. Thus, it is critically important to identify whether proteins themselves interact with each other. Although there are some existing experimental methods for self-interaction recognition, the limitations of these methods are both expensive and time-consuming. Therefore, it is very necessary to develop an efficient and stable computational method for predicting SIPs. In this study, we develop an effective computational method for predicting SIPs based on rotation forest (RF) classifier, combined with histogram of oriented gradients (HOG) and synthetic minority oversampling technique (SMOTE). When performing SIPs prediction on yeast and human datasets, the proposed method achieves superior accuracies of 97.28% and 89.41%, respectively. In addition, the proposed approach was compared with the state-of-the-art support vector machine (SVM) classifiers and other different methods on the same datasets. The experimental results demonstrate that our method has good robustness and effectiveness and can be regarded as a useful tool for SIPs prediction.

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Recognition of the stomach cancer images with probabilistic HOG feature vector histograms by using HOG features

Cited by 45 publications

References 5 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

Self-Interacting Proteins Prediction from PSSM Based on Evolutionary Information

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