Classification of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early stomach cancer detection

Korkmaz, Sevcan Aytac; Binol, Hamidullah

doi:10.1016/j.molstruc.2017.11.093

Cited by 40 publications

(20 citation statements)

References 21 publications

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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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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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“…and N is the number of trees that will be developed to determine the best division. The starting m value is randomly selected by the user [15,16]. The next m's are increased or decreased according to the generalized fault.…”

Section: Random Forest (Rf) Classifiermentioning

confidence: 99%

“…The RF method uses the Gini index. Gini measurements determine the cleavage position having the smallest Gini index [15,16]. Two parameters defined by the user are required to produce a tree with the RF classifier.…”

Section: Random Forest (Rf) Classifiermentioning

confidence: 99%

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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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“…Usually, image classification consists of data pre-processing, feature selection, and extraction. Image classification is a new technology, including image acquisition, image pre-processes feature extraction, and judgment [11]. At present, image classification methods include pixel and feature-based methods.…”

Section: Introductionmentioning

confidence: 99%

An Improved Residual-Network Model-based Conditional Generative Adversarial Network Plantar Pressure Image Classification: A Comparison of Normal, Planus, and Talipes Equinovarus Feet

Han

Wang

et al. 2021

Preprint

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The number of layers of deep learning (DL) increases, and following the performance of computing nodes improvement, the output accuracy of deep neural networks (DNN) faces a bottleneck problem. The resident network (RN) based DNN model was applied to address these issues recently. This paper improved the RN and developed a rectified linear unit (ReLU) based conditional generative adversarial nets (cGAN) to classify plantar pressure images. A foot scan system collected the plantar pressure images, in which normal (N), planus (PL), and talipes equinovarus feet (TE) data-sets were acquired subsequently. The 9-foot types named N, PL, TE, N-PL, N-TE, PL-N, PL-TE, TE-N, and TE-PL were classified using the proposed DNN models, named resident network-based conditional generative adversarial nets (RNcGAN). It improved the RN structure firstly and the cGAN system hereafter. In the classification of plantar pressure images, the pixel-level state matrix can be direct as an input, which is different from the previous image classification task with image reduction and feature extraction. cGAN can directly output the pixels of the image without any simplification. Finally, the model achieved better results in the evaluation indicators of accuracy (AC), sensitivity (SE), and F1-measurement (F1) by comparing to artificial neural networks (ANN), k-nearest neighbor (kNN), Fast Region-based Convolution Neural Network (Fast R-CNN), visual geometry group (VGG16), scaled-conjugate-gradient convolution neural networks (SCG-CNN), GoogleNet, AlexNet, ResNet-50-177, and Inception-v3. The final prediction of class accuracy is 95.17%. Foot type classification is vital for producing comfortable shoes in the industry.

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Classification of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early stomach cancer detection

Cited by 40 publications

References 21 publications

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

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

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

An Improved Residual-Network Model-based Conditional Generative Adversarial Network Plantar Pressure Image Classification: A Comparison of Normal, Planus, and Talipes Equinovarus Feet

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