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EfficientNetB0 and SVM Based Approach for Colon Cancer Recognition from Histopathological Images

Year 2023, Volume: 35 Issue: 2, 771 - 781, 01.09.2023
https://doi.org/10.35234/fumbd.1323422

Abstract

Colon cancer is a significant health issue in developed countries and ranks among the most common types of cancer. Early diagnosis of this disease increases the chances of survival for patients, while delayed diagnosis can lead to fatal outcomes. In this study, an EfficientNetB0 and Support Vector Machines (SVM) based model has been proposed for colon cancer detection. The EfficientNetB0 architecture is utilized to extract feature maps from histopathological images, and the SVM algorithm is employed to classify the obtained feature maps. Furthermore, to analyze the performance of the proposed model, a comparison is made with convolutional neural network (CNN) architectures such as EfficientNetB0, Xception, VGG19, InceptionV3, DenseNet121, and ResNet101. The datasets used for the study are the eight-class Kather-5k and the two-class LC25000 datasets. The findings indicate that the proposed model achieves higher success rates compared to existing CNN architectures on the Kather-5k dataset, with an accuracy of 99.70%, precision of 100%, recall of 100%, F1-Score of 100%, G-mean of 99.71%, specificity of 100%, and an AUC of 99.83%. Similarly, on the LC25000 dataset, the proposed model achieves 100% success rates in all metrics. When the combined dataset of Kather-5k and LC25000 is used, the proposed model demonstrates better performance compared to other models with an accuracy of 99.96%, precision of 100%, recall of 100%, F1-Score of 100%, G-mean of 99.92%, specificity of 100%, and an AUC of 99.96%. In addition, with the proposed model, a significant increase in success has been achieved in the success of the EfficientNetB0 architecture.

References

  • Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA. Cancer J. Clin 2021; 71(3): 209–249.
  • Pacal I, Karaboga D, Basturk A, Akay B, Nalbantoglu U. A comprehensive review of deep learning in colon cancer. Comput. Biol. Med. 2020; 126(April): 104003.
  • Xi Y, Xu P. Global colorectal cancer burden in 2020 and projections to 2040. Transl. Oncol. 2021; 14(10): 101174.
  • Dabass M, Vashisth S, Vig R. A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images. Comput. Biol. Med. 2002; 147: 105680.
  • Liang M, Ren Z, Yang J, Feng W, Li B. Identification of Colon Cancer Using Multi-Scale Feature Fusion Convolutional Neural Network Based on Shearlet Transform. IEEE Access. 2020; 8; 08969–208977.
  • Sarwinda D, Paradisa RH, Bustamam A, Anggia P. Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer. Procedia Comput. Sci. 2021; 179: 423–431.
  • Catal H, Veysel R. Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database. J. Digit. Imaging. 2023; 36 (1): 306–325.
  • Mehedi FMJ, Newaz A, Alam H, Binte S. Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification. 2021.
  • Cancer Survival Rates. (Erişim Tarihi 18 Nisan 2023). https://cancersurvivalrates.com/?type=colon&role=patient.
  • Sánchez-peralta LF, Bote-curiel L, Picón A, Sánchez-margallo FM, Pagador JB. Deep learning to find colorectal polyps in colonoscopy : A systematic literature review. Artif. Intell. Med.2020; 108(August): 101923.
  • Rathore S, Hussain M, Khan A. Automated colon cancer detection using hybrid of novel geometric features and some traditional features. Comput. Biol. Med. 2015; 65; 279–296.
  • Toraman S, Girgin M, Üstündağ B, Türkoğlu İ. Classification of the likelihood of colon cancer with machine learning techniquesusing FTIR signals obtained from plasma. TURKISH J. Electr. Eng. Comput. Sci. 2019; 27(3): 1765–1779.
  • Toğaçar M. Disease type detection in lung and colon cancer images using the complement approach of inefficient sets. Comput. Biol. Med. 2021; 137: 104827.
  • Garg S, Garg S. Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps. in 2020 3rd Artificial Intelligence and Cloud Computing Conference. Dec. 2020; 38–45.
  • Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018; 155(4); 1069-1078.
  • Babu T, Gupta D, Singh T, Hameed S. Colon Cancer Prediction On Different Magnified Colon Biopsy Images. in 2018 Tenth International Conference on Advanced Computing (ICoAC); 13-15 December 2018; Chennai, Indiap. 277–280.
  • Masud M, Sikder N, Nahid AA, Bairagi AK, AlZain MA. A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework. Sensors; 21(3): 748.
  • Jiao L, Chen Q, Li S, Xu Y. Colon Cancer Detection Using Whole Slide Histopathological Images. 2013; 1283–1286.
  • Tasnim Z, Chakraborty S, Shamrat, Mehedi FMJ, Chowdhury AN, Nuha HA, Karim A, Zahir SB, Billah MdM. Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification. Int. J. Adv. Comput. Sci. Appl. 2021; 12(8).
  • Qasim Y, Al-Sameai H, Ali O, Hassan A. Convolutional Neural Networks for Automatic Detection of Colon Adenocarcinoma Based on Histopathological Images. 2021; 19–28.
  • Doğan G, Ergen B. A new approach based on convolutional neural network and feature selection for recognizing vehicle types. Iran J. Comput. Sci. 2023; 6(2): 95–105.
  • İmak A, Doğan G, Şengür A, Ergen B. Asma Yaprağı Türünün Sınıflandırılması için Doğal ve Sentetik Verilerden Derin Öznitelikler Çıkarma, Birleştirme ve Seçmeye Dayalı Yeni Bir Yöntem. Int. J. Pure Appl. Sci. 2023; 9(1): 46–55.
  • Kather JN,Weis C, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Zöllner FG. Multi-class texture analysis in colorectal cancer histology. Sci. Rep.2016; 6(1): 27988.
  • Kather JN, Zöllner FG, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Weis CA. Collection of textures in colorectal cancer histology. Zenodo. 2016.
  • Borkowski AA, Bui MM, Thomas LB, Wilson CP, DeLand LA, Mastorides SM. Lung and Colon Cancer Histopathological Image Dataset (LC25000). arXiv. 2019.
  • LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521(7553); 436–444.
  • Behrad F, Saniee Abadeh M. An overview of deep learning methods for multimodal medical data mining. Expert Syst. Appl. 2022; 200(Aug.):117006.
  • Sharma A, Lysenko A, Boroevich KA, Vans E, Tsunoda T. DeepFeature: feature selection in nonimage data using convolutional neural network. Brief. Bioinform. 2021; 22(6).
  • Li Z, Liu F, Yang W, Peng S, Zhou J. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Trans. Neural Networks Learn. Syst. 2022; 33(12): 6999–7019.
  • Subramanian N, Elharrouss O, Al-Maadeed S, Chowdhury M. A review of deep learning-based detection methods for COVID-19. Comput. Biol. Med. 2022; 143: 105233.
  • Sarıgül M, Ozyildirim BM, Avci M. Differential convolutional neural network. Neural Networks. 2019; 116: 279–287.
  • Doğan G, Ergen B. Karayollarındaki Asfalt Çatlaklarının Tespiti İçin Yeni Bir Konvolüsyonel Sinir Ağı Tabanlı Yöntem. Fırat Üniversitesi Mühendislik Bilim. Derg. 2022; 34(2): 485–494.
  • Çalışan M, Talu MF. Comparison of Methods for Determining Activity from Physical Movements. Politek. Derg. 2021; 24(1): 17–23.
  • Özdemir E, Türkoğlu İ. Yazılım Güvenlik Açıklarının Evrişimsel Sinir Ağları (CNN) ile Sınıflandırılması. Fırat Üniversitesi Mühendislik Bilim. Derg. 2022; 34(2): 517–529.
  • Celik G. CovidCoughNet: A new method based on convolutional neural networks and deep feature extraction using pitch-shifting data augmentation for covid-19 detection from cough, breath, and voice signals. Comput. Biol. Med. 2023; 163: 107153.
  • Toğaçar, M., Cömert, Z. & Ergen, B. Enhancing of dataset using DeepDream, fuzzy color image enhancement and hypercolumn techniques to detection of the Alzheimer's disease stages by deep learning model. Neural Comput & Applic. 2021; 33, 9877–9889.
  • Başaran E. A new brain tumor diagnostic model: Selection of textural feature extraction algorithms and convolution neural network features with optimization algorithms. Comput. Biol. Med. 2022; 148; 105857.
  • Çalışkan, A. Detecting human activity types from 3D posture data using deep learning models. Biomed. Signal Process. Control. 2023; 81:104479.
  • Çalışkan A. Classification of Tympanic Membrane Images based on VGG16 Model. Kocaeli J. Sci. Eng. 2022; 5(1): 105–111.
  • Toğaçar, Z. Cömert, B. Ergen and Ü. Budak, "Brain Hemorrhage Detection based on Heat Maps, Autoencoder and CNN Architecture," 2019 1st International Informatics and Software Engineering Conference (UBMYK), Ankara, Turkey. 2019; 1-5.
  • Tan M, Le QV. EfficientNet : Rethinking Model Scaling for Convolutional Neural Networks. in Proceedings of the 36th International Conference on Machine Learning, PMLR 97. 9-15 June 2019; Long Beach, California: 6105–6114.
  • Alhichri H, Alswayed AS, Bazi Y, Ammour N, Alajlan NA. Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention. IEEE Access. 2021; 9: 14078–14094.
  • Gang S, Fabrice N, Chung D, Lee J. Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning. Sensors. 2021; 21(9): 2921.
  • Shahbakhi M, Far DT, Tahami E. Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine. J. Biomed. Sci. Eng. 2014; 07(04): 147–156.
  • Gunduz H. Deep Learning-Based Parkinson’s Disease Classification Using Vocal Feature Sets. IEEE Access. 2019; 7: 115540–115551.
  • Thangavel KD, Seerengasamy U, Palaniappan S, Sekar R. Prediction of factors for Controlling of Green House Farming with Fuzzy based multiclass Support Vector Machine. Alexandria Eng. J. 2023; 62: 279–289.
  • Başaran E. Classification of white blood cells with SVM by selecting SqueezeNet and LIME properties by mRMR method. Signal, Image Video Process. 2022; 16(7): 1821–1829.
  • Kaya Y, Uyar M. A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease Appl. Soft Comput. J. 2013; 13(8); 3429–3438.
  • Liu T, Fan W, Wu C. A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset. Artif. Intell. Med. 2019; 101(September): 101723.
  • Caprolu M, Raponi S, Oligeri G, Di Pietro R. Cryptomining makes noise : Detecting cryptojacking via Machine Learning. Comput. Commun. 2020; 171(November): 126–139.
  • Subramanian N, Elharrouss O, Al-maadeed S, Chowdhury M. A review of deep learning-based detection methods for COVID-19. Comput. Biol. Med. 2022; 143: 105233.
  • Ibrahim AA, Ridwan RL, Muhammed MM, Abdulaziz RO, Saheed GA. Comparison of the CatBoost Classifier with other Machine Learning Methods. Int. J. Adv. Comput. Sci. Appl. 2020; 11(11): 738–748.
  • Ghosh S, Bandyopadhyay A, Sahay S, Ghosh R, Kundu I, Santosh KC. Colorectal Histology Tumor Detection Using Ensemble Deep Neural Network. Eng. Appl. Artif. Intell. 2021; 100: 104202.
  • Trivizakis E, Ioannidis GS, Souglakos I, Karantanas AH, Tzardi M, Marias K. A neural pathomics framework for classifying colorectal cancer histopathology images based on wavelet multi-scale texture analysis. Sci. Rep. 2021; 11(1): 15546.
  • Tsai MJ, Tao YH. Deep Learning Techniques for the Classification of Colorectal Cancer Tissue. Electronics. 2021; 10(14): 1662.
  • Paladini E, Vantaggiato E, Bougourzi F, Distante C, Hadid A, Taleb-Ahmed A. Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification. J. Imaging. 2021; 7(3): 51.

Histopatolojik Görüntülerden Kolon Kanseri Tespiti için EfficientNetB0 ve DVM Tabanlı Yaklaşım

Year 2023, Volume: 35 Issue: 2, 771 - 781, 01.09.2023
https://doi.org/10.35234/fumbd.1323422

Abstract

Kolon kanseri, gelişmiş ülkelerde ciddi bir sağlık sorunu olmakta ve en sık görülen kanser türleri arasında gelmektedir. Bu hastalığın erken teşhisi hastaların hayatta kalma şansını artırmaktadır. Geciken teşhisler ise ölümle sonuçlanabilmektedir. Bu çalışmada kolon kanseri tespiti için EfficientNetB0 ve destek vektör makineleri (DVM) tabanlı bir model önerilmiştir. EfficientNetB0 mimarisi ile histopatolojik görüntülerden öznitelik haritalarının çıkarılması sağlanırken, DVM algoritması ile elde edilen öznitelik haritalarının sınıflandırılması gerçekleştirilmektedir. Ayrıca önerilen modelin başarısını analiz etmek üzere EfficientNetB0, Xception, VGG19, InceptionV3, DenseNet121 ve ResNet101 gibi evrişimli sinir ağları (ESA) mimarileri ile performans kıyaslaması yapılmıştır. Veri kümesi olarak sekiz sınıflı Kather-5k ve iki sınıflı LC25000 veri kümeleri kullanılmıştır. Elde edilen bulgular, önerilen modelin Kather-5k veri kümesi kullanıldığında %99.70 doğruluk, %100 kesinlik, %100 duyarlılık, %100 F1-Score, %99.71 G-ortalama, %100 özgüllük ve %99.83 AUC ile mevcut ESA mimarilerine kıyasla daha yüksek başarı sağladığını göstermiştir. LC25000 veri kümesi kullanıldığında ise önerilen model tüm metriklerde %100 başarı elde etmiştir. Benzer şekilde Kather-5k ve LC25000 veri kümeleri birleşiminden oluşan veri kümesi kullanıldığında önerilen model, %99.96 doğruluk, %100 kesinlik, %100 duyarlılık, %100 F1-Score, %99.92 G-ortalama, %100 özgüllük ve %99.96 AUC oranı ile diğer modellere kıyasla daha yüksek performans göstermiştir. Ayrıca önerilen model ile EfficientNetB0 mimarisinin başarısında önemli oranda bir başarı artışı sağlanmıştır.

References

  • Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA. Cancer J. Clin 2021; 71(3): 209–249.
  • Pacal I, Karaboga D, Basturk A, Akay B, Nalbantoglu U. A comprehensive review of deep learning in colon cancer. Comput. Biol. Med. 2020; 126(April): 104003.
  • Xi Y, Xu P. Global colorectal cancer burden in 2020 and projections to 2040. Transl. Oncol. 2021; 14(10): 101174.
  • Dabass M, Vashisth S, Vig R. A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images. Comput. Biol. Med. 2002; 147: 105680.
  • Liang M, Ren Z, Yang J, Feng W, Li B. Identification of Colon Cancer Using Multi-Scale Feature Fusion Convolutional Neural Network Based on Shearlet Transform. IEEE Access. 2020; 8; 08969–208977.
  • Sarwinda D, Paradisa RH, Bustamam A, Anggia P. Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer. Procedia Comput. Sci. 2021; 179: 423–431.
  • Catal H, Veysel R. Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database. J. Digit. Imaging. 2023; 36 (1): 306–325.
  • Mehedi FMJ, Newaz A, Alam H, Binte S. Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification. 2021.
  • Cancer Survival Rates. (Erişim Tarihi 18 Nisan 2023). https://cancersurvivalrates.com/?type=colon&role=patient.
  • Sánchez-peralta LF, Bote-curiel L, Picón A, Sánchez-margallo FM, Pagador JB. Deep learning to find colorectal polyps in colonoscopy : A systematic literature review. Artif. Intell. Med.2020; 108(August): 101923.
  • Rathore S, Hussain M, Khan A. Automated colon cancer detection using hybrid of novel geometric features and some traditional features. Comput. Biol. Med. 2015; 65; 279–296.
  • Toraman S, Girgin M, Üstündağ B, Türkoğlu İ. Classification of the likelihood of colon cancer with machine learning techniquesusing FTIR signals obtained from plasma. TURKISH J. Electr. Eng. Comput. Sci. 2019; 27(3): 1765–1779.
  • Toğaçar M. Disease type detection in lung and colon cancer images using the complement approach of inefficient sets. Comput. Biol. Med. 2021; 137: 104827.
  • Garg S, Garg S. Prediction of lung and colon cancer through analysis of histopathological images by utilizing Pre-trained CNN models with visualization of class activation and saliency maps. in 2020 3rd Artificial Intelligence and Cloud Computing Conference. Dec. 2020; 38–45.
  • Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018; 155(4); 1069-1078.
  • Babu T, Gupta D, Singh T, Hameed S. Colon Cancer Prediction On Different Magnified Colon Biopsy Images. in 2018 Tenth International Conference on Advanced Computing (ICoAC); 13-15 December 2018; Chennai, Indiap. 277–280.
  • Masud M, Sikder N, Nahid AA, Bairagi AK, AlZain MA. A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework. Sensors; 21(3): 748.
  • Jiao L, Chen Q, Li S, Xu Y. Colon Cancer Detection Using Whole Slide Histopathological Images. 2013; 1283–1286.
  • Tasnim Z, Chakraborty S, Shamrat, Mehedi FMJ, Chowdhury AN, Nuha HA, Karim A, Zahir SB, Billah MdM. Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification. Int. J. Adv. Comput. Sci. Appl. 2021; 12(8).
  • Qasim Y, Al-Sameai H, Ali O, Hassan A. Convolutional Neural Networks for Automatic Detection of Colon Adenocarcinoma Based on Histopathological Images. 2021; 19–28.
  • Doğan G, Ergen B. A new approach based on convolutional neural network and feature selection for recognizing vehicle types. Iran J. Comput. Sci. 2023; 6(2): 95–105.
  • İmak A, Doğan G, Şengür A, Ergen B. Asma Yaprağı Türünün Sınıflandırılması için Doğal ve Sentetik Verilerden Derin Öznitelikler Çıkarma, Birleştirme ve Seçmeye Dayalı Yeni Bir Yöntem. Int. J. Pure Appl. Sci. 2023; 9(1): 46–55.
  • Kather JN,Weis C, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Zöllner FG. Multi-class texture analysis in colorectal cancer histology. Sci. Rep.2016; 6(1): 27988.
  • Kather JN, Zöllner FG, Bianconi F, Melchers SM, Schad LR, Gaiser T, Marx A, Weis CA. Collection of textures in colorectal cancer histology. Zenodo. 2016.
  • Borkowski AA, Bui MM, Thomas LB, Wilson CP, DeLand LA, Mastorides SM. Lung and Colon Cancer Histopathological Image Dataset (LC25000). arXiv. 2019.
  • LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521(7553); 436–444.
  • Behrad F, Saniee Abadeh M. An overview of deep learning methods for multimodal medical data mining. Expert Syst. Appl. 2022; 200(Aug.):117006.
  • Sharma A, Lysenko A, Boroevich KA, Vans E, Tsunoda T. DeepFeature: feature selection in nonimage data using convolutional neural network. Brief. Bioinform. 2021; 22(6).
  • Li Z, Liu F, Yang W, Peng S, Zhou J. A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects. IEEE Trans. Neural Networks Learn. Syst. 2022; 33(12): 6999–7019.
  • Subramanian N, Elharrouss O, Al-Maadeed S, Chowdhury M. A review of deep learning-based detection methods for COVID-19. Comput. Biol. Med. 2022; 143: 105233.
  • Sarıgül M, Ozyildirim BM, Avci M. Differential convolutional neural network. Neural Networks. 2019; 116: 279–287.
  • Doğan G, Ergen B. Karayollarındaki Asfalt Çatlaklarının Tespiti İçin Yeni Bir Konvolüsyonel Sinir Ağı Tabanlı Yöntem. Fırat Üniversitesi Mühendislik Bilim. Derg. 2022; 34(2): 485–494.
  • Çalışan M, Talu MF. Comparison of Methods for Determining Activity from Physical Movements. Politek. Derg. 2021; 24(1): 17–23.
  • Özdemir E, Türkoğlu İ. Yazılım Güvenlik Açıklarının Evrişimsel Sinir Ağları (CNN) ile Sınıflandırılması. Fırat Üniversitesi Mühendislik Bilim. Derg. 2022; 34(2): 517–529.
  • Celik G. CovidCoughNet: A new method based on convolutional neural networks and deep feature extraction using pitch-shifting data augmentation for covid-19 detection from cough, breath, and voice signals. Comput. Biol. Med. 2023; 163: 107153.
  • Toğaçar, M., Cömert, Z. & Ergen, B. Enhancing of dataset using DeepDream, fuzzy color image enhancement and hypercolumn techniques to detection of the Alzheimer's disease stages by deep learning model. Neural Comput & Applic. 2021; 33, 9877–9889.
  • Başaran E. A new brain tumor diagnostic model: Selection of textural feature extraction algorithms and convolution neural network features with optimization algorithms. Comput. Biol. Med. 2022; 148; 105857.
  • Çalışkan, A. Detecting human activity types from 3D posture data using deep learning models. Biomed. Signal Process. Control. 2023; 81:104479.
  • Çalışkan A. Classification of Tympanic Membrane Images based on VGG16 Model. Kocaeli J. Sci. Eng. 2022; 5(1): 105–111.
  • Toğaçar, Z. Cömert, B. Ergen and Ü. Budak, "Brain Hemorrhage Detection based on Heat Maps, Autoencoder and CNN Architecture," 2019 1st International Informatics and Software Engineering Conference (UBMYK), Ankara, Turkey. 2019; 1-5.
  • Tan M, Le QV. EfficientNet : Rethinking Model Scaling for Convolutional Neural Networks. in Proceedings of the 36th International Conference on Machine Learning, PMLR 97. 9-15 June 2019; Long Beach, California: 6105–6114.
  • Alhichri H, Alswayed AS, Bazi Y, Ammour N, Alajlan NA. Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention. IEEE Access. 2021; 9: 14078–14094.
  • Gang S, Fabrice N, Chung D, Lee J. Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning. Sensors. 2021; 21(9): 2921.
  • Shahbakhi M, Far DT, Tahami E. Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine. J. Biomed. Sci. Eng. 2014; 07(04): 147–156.
  • Gunduz H. Deep Learning-Based Parkinson’s Disease Classification Using Vocal Feature Sets. IEEE Access. 2019; 7: 115540–115551.
  • Thangavel KD, Seerengasamy U, Palaniappan S, Sekar R. Prediction of factors for Controlling of Green House Farming with Fuzzy based multiclass Support Vector Machine. Alexandria Eng. J. 2023; 62: 279–289.
  • Başaran E. Classification of white blood cells with SVM by selecting SqueezeNet and LIME properties by mRMR method. Signal, Image Video Process. 2022; 16(7): 1821–1829.
  • Kaya Y, Uyar M. A hybrid decision support system based on rough set and extreme learning machine for diagnosis of hepatitis disease Appl. Soft Comput. J. 2013; 13(8); 3429–3438.
  • Liu T, Fan W, Wu C. A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset. Artif. Intell. Med. 2019; 101(September): 101723.
  • Caprolu M, Raponi S, Oligeri G, Di Pietro R. Cryptomining makes noise : Detecting cryptojacking via Machine Learning. Comput. Commun. 2020; 171(November): 126–139.
  • Subramanian N, Elharrouss O, Al-maadeed S, Chowdhury M. A review of deep learning-based detection methods for COVID-19. Comput. Biol. Med. 2022; 143: 105233.
  • Ibrahim AA, Ridwan RL, Muhammed MM, Abdulaziz RO, Saheed GA. Comparison of the CatBoost Classifier with other Machine Learning Methods. Int. J. Adv. Comput. Sci. Appl. 2020; 11(11): 738–748.
  • Ghosh S, Bandyopadhyay A, Sahay S, Ghosh R, Kundu I, Santosh KC. Colorectal Histology Tumor Detection Using Ensemble Deep Neural Network. Eng. Appl. Artif. Intell. 2021; 100: 104202.
  • Trivizakis E, Ioannidis GS, Souglakos I, Karantanas AH, Tzardi M, Marias K. A neural pathomics framework for classifying colorectal cancer histopathology images based on wavelet multi-scale texture analysis. Sci. Rep. 2021; 11(1): 15546.
  • Tsai MJ, Tao YH. Deep Learning Techniques for the Classification of Colorectal Cancer Tissue. Electronics. 2021; 10(14): 1662.
  • Paladini E, Vantaggiato E, Bougourzi F, Distante C, Hadid A, Taleb-Ahmed A. Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification. J. Imaging. 2021; 7(3): 51.
There are 56 citations in total.

Details

Primary Language Turkish
Subjects Deep Learning
Journal Section MBD
Authors

Gaffari Çelik 0000-0001-5658-9529

Publication Date September 1, 2023
Submission Date July 6, 2023
Published in Issue Year 2023 Volume: 35 Issue: 2

Cite

APA Çelik, G. (2023). Histopatolojik Görüntülerden Kolon Kanseri Tespiti için EfficientNetB0 ve DVM Tabanlı Yaklaşım. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 35(2), 771-781. https://doi.org/10.35234/fumbd.1323422
AMA Çelik G. Histopatolojik Görüntülerden Kolon Kanseri Tespiti için EfficientNetB0 ve DVM Tabanlı Yaklaşım. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. September 2023;35(2):771-781. doi:10.35234/fumbd.1323422
Chicago Çelik, Gaffari. “Histopatolojik Görüntülerden Kolon Kanseri Tespiti için EfficientNetB0 Ve DVM Tabanlı Yaklaşım”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 35, no. 2 (September 2023): 771-81. https://doi.org/10.35234/fumbd.1323422.
EndNote Çelik G (September 1, 2023) Histopatolojik Görüntülerden Kolon Kanseri Tespiti için EfficientNetB0 ve DVM Tabanlı Yaklaşım. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 35 2 771–781.
IEEE G. Çelik, “Histopatolojik Görüntülerden Kolon Kanseri Tespiti için EfficientNetB0 ve DVM Tabanlı Yaklaşım”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 35, no. 2, pp. 771–781, 2023, doi: 10.35234/fumbd.1323422.
ISNAD Çelik, Gaffari. “Histopatolojik Görüntülerden Kolon Kanseri Tespiti için EfficientNetB0 Ve DVM Tabanlı Yaklaşım”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 35/2 (September 2023), 771-781. https://doi.org/10.35234/fumbd.1323422.
JAMA Çelik G. Histopatolojik Görüntülerden Kolon Kanseri Tespiti için EfficientNetB0 ve DVM Tabanlı Yaklaşım. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2023;35:771–781.
MLA Çelik, Gaffari. “Histopatolojik Görüntülerden Kolon Kanseri Tespiti için EfficientNetB0 Ve DVM Tabanlı Yaklaşım”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 35, no. 2, 2023, pp. 771-8, doi:10.35234/fumbd.1323422.
Vancouver Çelik G. Histopatolojik Görüntülerden Kolon Kanseri Tespiti için EfficientNetB0 ve DVM Tabanlı Yaklaşım. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2023;35(2):771-8.