Araştırma Makalesi
LBP Özelliklerine Dayanan Lokasyon Koruyan Projeksiyon (LPP) Boyut Azaltma Metodunun Farklı Sınıflandırıcılar Üzerindeki Performanslarının Karşılaştırılması
Öz
Görüntü hücreleri
Işık Mikroskop yardımıyla alınmıştır. Yerel ikili örüntü (LBP) özellikleri
orijinal görüntüler için elde edilmiştir. Bu görüntülerin LBP özelliklerinin yüksek
boyutu, Lokasyon Koruyan Projeksiyon (LPP) ile daha düşük boyuta indirgenir. Bu
düşük boyutlu veriler Rastgele Orman (RF), Naive Bayes (NB) ve Yapay Sinir
Ağları (ANN) sınıflandırıcısı tarafından sınıflandırılmıştır. Yapılan
sınıflandırma sonuçları daha önceden yapılan çalışmalar ile
karşılaştırılmıştır. ANN sınıflandırıcısıyla elde edilen performans RF ve NB
sınıflandırıcına göre daha yüksektir. Üstelik, ANN sınıflandırıcısında kullanılan
özellik vektör boyutu, RF ve NB sınıflandırıcılarında kullanılan özellik
vektörü boyutundan daha düşüktür. LPP Yöntemine göre ANN, RF ve NB
sınıflandırıcıları ile elde edilen başarı oranları sırasıyla% 96.29,% 74.44 ve%
70.00'dır.
Anahtar Kelimeler
yerel ikili örüntü rastgele orman lpp yapay sinir ağları naive bayes
Kaynakça
- Ural, Berkan, et al. "Gastric Cancer Regional Detection System," Journal of medical systems, vol. 40, no. 1, pp. 31, 2016.
- Hirayama, Akiyoshi, et al. "Quantitative metabolome profiling of colon and Gastric cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry," Cancer research, vol.69, no. 11, pp. 4918-4925, 2009
- Brenner, Hermann, Dietrich Rothenbacher, and Volker Arndt. "Epidemiology of Gastric cancer," Cancer pidemiology: Modifiable Factors, pp. 467-477, 2009.
- Fujioka, Naoko, et al. "Discrimination between normal and malignant human gastric tissues by Fourier transform infrared spectroscopy," Cancer Detection and Prevention, vol. 28, no.1, pp.32-36, 2004.
- Sasaki, Yoshihiro, et al. "Computer-aided estimation for the risk of development of gastric cancer by image processing," Artificial Intelligence in Theory and Practice III, pp. 197-204, 2010.
- Ahmadzadeh, D., Fiuzy, M., Haddadnia, J. “Gastric Cancer Diagnosis by Using a Combination of Image Processing Algorithms, Local Binary Pattern Algorithm and Support Vector Machine,” Journal of Basic and Applied ScientificRe
- Akbari, Hamed, et al. "Cancer detection using infrared hyperspectral imaging." Cancer science, vol. 102, no. 4, pp. 852-857, 2011.
- Tannapfel, Andrea, et al. "Expression of the p53 homologues p63 and p73 in multiple simultaneous gastric cancer." The Journal of pathology, vol. 195, no. 2, pp. 163-170, 2001.
- Malkapurkar, AnaghaV, Rupali Patil, and Sachin Murarka. "A new technique for LBP method to improve face recognition." International Journal of Emerging Technology and Advanced Engineering, vol. 1, no.1, pp. 67-71, 2011.
- T. Ojala, M. Pietik¨ainen and D. Harwood, “A comparative study of texture measures with classification based on feature distributions” Pattern Recognition, vol. 29, 1996.
- He, Xiaofei, and Partha Niyogi. "Locality preserving projections," Advances in neural information processing systems, pp. 153-160, 2004.
- Yildiz, Eray, and Yusuf Sevim. "Comparison of linear dimensionality reduction methods on classification methods." Electrical, Electronics and Biomedical Engineering (ELECO), National Conference on. IEEE, 2016.
- Wang, Z., & He, B. “Locality perserving projections algorithm for hyperspectral image dimensionality reduction,” In Geoinformatics, 2011 19th International Conference on, pp. 1-4, IEEE, 2011.
- Jin, Xin, et al. "Locality preserving projection on source code metrics for improved software maintainability, " Advances in Artificial Intelligence, pp. 877-886, 2006.
- Pal, Mahesh. "Random forest classifier for remote sensing classification." International Journal of Remote Sensing, vol. 26, no. 1, pp. 217-222, 2005.
- Korkmaz, Sevcan Aytaç, and Hamidullah Binol. "Analysis of Molecular Structure Images by using ANN, RF, LBP, HOG, and Size Reduction Methods for early Stomach Cancer Detection."Journal of Molecular Structure (2017).
- http://www.atasoyweb.net/Geri-Yayilimli-Yapay-Sinir-Aglari. 25.12.2017.
- McCallum, Andrew, and Kamal Nigam. "A comparison of event models for naive bayes text classification." AAAI-98 workshop on learning for text categorization, Vol. 752, pp. 41-48, 1998.
- McCallum, Andrew, and Kamal Nigam. "A comparison of event models for naive bayes text classification." AAAI-98 workshop on learning for text categorization, Vol. 752, pp. 41-48, 1998.
- Yongkui, S., Pengrui, L., Ying, W., Jingyu, Z., & Meijie, L. “The Prediction of the Caving Degree of Coal Seam Roof Based on the Naive Bayes Classifier,” Electronic Journal of Geotechnical Engineering, vol. 19, no. Z2, 201
- Korkmaz, Sevcan Aytac, et al. "Diagnosis of Breast Cancer Nano-Biomechanics Images Taken from Atomic Force Microscope," Journal of Nanoelectronics and Optoelectronics, vol.11, no. 4, pp. 551-559, 2016.
- Korkmaz, Sevcan Aytaç, et al. "A expert system for stomach cancer images with artificial neural network by using HOG features and linear discriminant analysis: HOG_LDA_ANN." Intelligent Systems and Informatics (SISY), 2017
- Korkmaz, Sevcan Aytaç, et al. "Recognition of the stomach cancer images with probabilistic HOG feature vector histograms by using HOG features," Intelligent Systems and Informatics (SISY), 2017 IEEE 15th International Sym
- Korkmaz, Sevcan Aytac, Mehmet Fatih Korkmaz, and Mustafa Poyraz. "Diagnosis of breast cancer in light microscopic and mammographic images textures using relative entropy via kernel estimation." Medical & biological enginee
- Korkmaz, S. A., & Korkmaz, M. F. “ A new method based cancer detection in mammogram textures by finding feature weights and using Kullback–Leibler measure with kernel estimation,” Optik-International Journal for Light and
- Korkmaz, S. Aytac, and M. Poyraz. "A New Method Based for Diagnosis of Breast Cancer Cells from Microscopic Images: DWEE—JHT." Journal of medical systems, vol. 38, no. 9, pp.1-9, 2014.
- Korkmaz, Sevcan Aytac, and Mustafa Poyraz. "Least Square Support Vector Machine and Minumum Redundacy Maximum Relavance for Diagnosis of Breast Cancer from Breast Microscopic Images." Procedia-Social and Behavioral Science
- Korkmaz, Sevcan AYTAÇ. "DETECTING CELLS USING IMAGE SEGMENTATION OF THE CERVICAL CANCER IMAGES TAKEN FROM SCANNING ELECTRON MICROSCOPE." The Online Journal of Science and Technology-October, vol. 7, no.4, 2017.
- Korkmaz, Sevcan Aytaç, et al. "New methods based on mRMR_LSSVM and mRMR_KNN for diagnosis of breast cancer from microscopic and mammography images of some patients." International Journal of Biomedical Engineering and Tech
- Korkmaz, Sevcan Aytaç, and Haluk Eren. "Cancer detection in mammograms estimating feature weights via Kullback-Leibler measure." Image and Signal Processing (CISP), 2013 6th International Congress on. Vol. 2., IEEE, 2013.
- Sengur, Abdulkadir, and Ibrahim Turkoglu. "A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases." Expert Systems with Applications, vol. 35, no. 3, pp. 1011-1020,
- Şengür, Abdülkadir, İbrahim Türkoğlu, and M. Cevdet İnce. "ENDOSKOPİK GÖRÜNTÜLERİN DEĞERLENDİRİLMESİNDE GÖRÜNTÜ İŞLEME TEMELLİ AKILLI BİR KARAR DESTEK SİSTEMİ." Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol.15,
- Özçift, Akın, and Arif Gülten. "Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases." Digital Signal Processing vol.23, no.1, pp. 230-237, 2013.
- Güler, Inan, et al. "Classification of aorta doppler signals using variable coded-hierarchical genetic fuzzy system." Expert Systems with Applications, vol. 26, no. 3, pp. 321-333, 2004.
Comparison of Performance on the Different Classifiers of the Locating Protected Projection (LPP) Dimension Reduction Method Based LBP Features
Öz
Image 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.
Anahtar Kelimeler
local binary pattern features random forest lpp Artificial Neural Networks Naive Bayes
Kaynakça
- Ural, Berkan, et al. "Gastric Cancer Regional Detection System," Journal of medical systems, vol. 40, no. 1, pp. 31, 2016.
- Hirayama, Akiyoshi, et al. "Quantitative metabolome profiling of colon and Gastric cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry," Cancer research, vol.69, no. 11, pp. 4918-4925, 2009
- Brenner, Hermann, Dietrich Rothenbacher, and Volker Arndt. "Epidemiology of Gastric cancer," Cancer pidemiology: Modifiable Factors, pp. 467-477, 2009.
- Fujioka, Naoko, et al. "Discrimination between normal and malignant human gastric tissues by Fourier transform infrared spectroscopy," Cancer Detection and Prevention, vol. 28, no.1, pp.32-36, 2004.
- Sasaki, Yoshihiro, et al. "Computer-aided estimation for the risk of development of gastric cancer by image processing," Artificial Intelligence in Theory and Practice III, pp. 197-204, 2010.
- Ahmadzadeh, D., Fiuzy, M., Haddadnia, J. “Gastric Cancer Diagnosis by Using a Combination of Image Processing Algorithms, Local Binary Pattern Algorithm and Support Vector Machine,” Journal of Basic and Applied ScientificRe
- Akbari, Hamed, et al. "Cancer detection using infrared hyperspectral imaging." Cancer science, vol. 102, no. 4, pp. 852-857, 2011.
- Tannapfel, Andrea, et al. "Expression of the p53 homologues p63 and p73 in multiple simultaneous gastric cancer." The Journal of pathology, vol. 195, no. 2, pp. 163-170, 2001.
- Malkapurkar, AnaghaV, Rupali Patil, and Sachin Murarka. "A new technique for LBP method to improve face recognition." International Journal of Emerging Technology and Advanced Engineering, vol. 1, no.1, pp. 67-71, 2011.
- T. Ojala, M. Pietik¨ainen and D. Harwood, “A comparative study of texture measures with classification based on feature distributions” Pattern Recognition, vol. 29, 1996.
- He, Xiaofei, and Partha Niyogi. "Locality preserving projections," Advances in neural information processing systems, pp. 153-160, 2004.
- Yildiz, Eray, and Yusuf Sevim. "Comparison of linear dimensionality reduction methods on classification methods." Electrical, Electronics and Biomedical Engineering (ELECO), National Conference on. IEEE, 2016.
- Wang, Z., & He, B. “Locality perserving projections algorithm for hyperspectral image dimensionality reduction,” In Geoinformatics, 2011 19th International Conference on, pp. 1-4, IEEE, 2011.
- Jin, Xin, et al. "Locality preserving projection on source code metrics for improved software maintainability, " Advances in Artificial Intelligence, pp. 877-886, 2006.
- Pal, Mahesh. "Random forest classifier for remote sensing classification." International Journal of Remote Sensing, vol. 26, no. 1, pp. 217-222, 2005.
- Korkmaz, Sevcan Aytaç, and Hamidullah Binol. "Analysis of Molecular Structure Images by using ANN, RF, LBP, HOG, and Size Reduction Methods for early Stomach Cancer Detection."Journal of Molecular Structure (2017).
- http://www.atasoyweb.net/Geri-Yayilimli-Yapay-Sinir-Aglari. 25.12.2017.
- McCallum, Andrew, and Kamal Nigam. "A comparison of event models for naive bayes text classification." AAAI-98 workshop on learning for text categorization, Vol. 752, pp. 41-48, 1998.
- McCallum, Andrew, and Kamal Nigam. "A comparison of event models for naive bayes text classification." AAAI-98 workshop on learning for text categorization, Vol. 752, pp. 41-48, 1998.
- Yongkui, S., Pengrui, L., Ying, W., Jingyu, Z., & Meijie, L. “The Prediction of the Caving Degree of Coal Seam Roof Based on the Naive Bayes Classifier,” Electronic Journal of Geotechnical Engineering, vol. 19, no. Z2, 201
- Korkmaz, Sevcan Aytac, et al. "Diagnosis of Breast Cancer Nano-Biomechanics Images Taken from Atomic Force Microscope," Journal of Nanoelectronics and Optoelectronics, vol.11, no. 4, pp. 551-559, 2016.
- Korkmaz, Sevcan Aytaç, et al. "A expert system for stomach cancer images with artificial neural network by using HOG features and linear discriminant analysis: HOG_LDA_ANN." Intelligent Systems and Informatics (SISY), 2017
- Korkmaz, Sevcan Aytaç, et al. "Recognition of the stomach cancer images with probabilistic HOG feature vector histograms by using HOG features," Intelligent Systems and Informatics (SISY), 2017 IEEE 15th International Sym
- Korkmaz, Sevcan Aytac, Mehmet Fatih Korkmaz, and Mustafa Poyraz. "Diagnosis of breast cancer in light microscopic and mammographic images textures using relative entropy via kernel estimation." Medical & biological enginee
- Korkmaz, S. A., & Korkmaz, M. F. “ A new method based cancer detection in mammogram textures by finding feature weights and using Kullback–Leibler measure with kernel estimation,” Optik-International Journal for Light and
- Korkmaz, S. Aytac, and M. Poyraz. "A New Method Based for Diagnosis of Breast Cancer Cells from Microscopic Images: DWEE—JHT." Journal of medical systems, vol. 38, no. 9, pp.1-9, 2014.
- Korkmaz, Sevcan Aytac, and Mustafa Poyraz. "Least Square Support Vector Machine and Minumum Redundacy Maximum Relavance for Diagnosis of Breast Cancer from Breast Microscopic Images." Procedia-Social and Behavioral Science
- Korkmaz, Sevcan AYTAÇ. "DETECTING CELLS USING IMAGE SEGMENTATION OF THE CERVICAL CANCER IMAGES TAKEN FROM SCANNING ELECTRON MICROSCOPE." The Online Journal of Science and Technology-October, vol. 7, no.4, 2017.
- Korkmaz, Sevcan Aytaç, et al. "New methods based on mRMR_LSSVM and mRMR_KNN for diagnosis of breast cancer from microscopic and mammography images of some patients." International Journal of Biomedical Engineering and Tech
- Korkmaz, Sevcan Aytaç, and Haluk Eren. "Cancer detection in mammograms estimating feature weights via Kullback-Leibler measure." Image and Signal Processing (CISP), 2013 6th International Congress on. Vol. 2., IEEE, 2013.
- Sengur, Abdulkadir, and Ibrahim Turkoglu. "A hybrid method based on artificial immune system and fuzzy k-NN algorithm for diagnosis of heart valve diseases." Expert Systems with Applications, vol. 35, no. 3, pp. 1011-1020,
- Şengür, Abdülkadir, İbrahim Türkoğlu, and M. Cevdet İnce. "ENDOSKOPİK GÖRÜNTÜLERİN DEĞERLENDİRİLMESİNDE GÖRÜNTÜ İŞLEME TEMELLİ AKILLI BİR KARAR DESTEK SİSTEMİ." Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, vol.15,
- Özçift, Akın, and Arif Gülten. "Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases." Digital Signal Processing vol.23, no.1, pp. 230-237, 2013.
- Güler, Inan, et al. "Classification of aorta doppler signals using variable coded-hierarchical genetic fuzzy system." Expert Systems with Applications, vol. 26, no. 3, pp. 321-333, 2004.
Toplam 34 adet kaynakça vardır.
Ayrıntılar
| Konular | Elektrik Mühendisliği |
|---|---|
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Ağustos 2018 |
| Gönderilme Tarihi | 6 Kasım 2017 |
| Kabul Tarihi | 25 Aralık 2017 |
| Yayımlandığı Sayı | Yıl 2018 Cilt: 22 Sayı: 4 |
Kaynak Göster
Cited By
A New Application Based on GPLVM, LMNN, and NCA for Early Detection of the Stomach Cancer
Applied Artificial Intelligence
Sevcan Aytaç Korkmaz
https://doi.org/10.1080/08839514.2018.1464285
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.