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Sıkma-Uyarma Artık Ağı kullanılarak Beyaz Kan Hücrelerinin Sınıflandırılması

Yıl 2023, , 189 - 205, 31.07.2023
https://doi.org/10.17671/gazibtd.1255477

Öz

Beyaz kan hücreleri, vücudun parazitler, bakteriler, virüsler gibi mikroorganizmalara karşı korunmasında etkin rol oynayan bağışıklık sisteminin önemli bir bileşenidir. Beyaz kan hücrelerinin yapısal özellikleri, alt türlerinin şekilleri ve sayıları insan sağlığı hakkında önemli bilgiler verebilmektedir. Hastalık teşhisinde doğru beyaz kan hücre tespiti klinik olarak oldukça önemlidir. Bu yüzden, doğru beyaz kan hücre sınıflandırma yöntemi kritik öneme sahiptir. Bu çalışmada, beyaz kan hücre sınıflandırması için Evrişimsel sinir ağı (ESA) tabanlı bir yöntem önerilmiştir. Önerilen yöntem sıkma-uyarma ağı ile artık ağ mimarisinin birleşiminden oluşan hibrit bir yöntemdir. Derin ağ mimarilerinde katman sayısı arttıkça oluşabilecek problemler artık ağ ile çözülebilmektedir. Sıkma-uyarma (SU) bloğunun artık ağ ile birlikte kullanımı, toplam parametre sayısını minimum düzeyde arttırırken sınıflandırma doğruluğunu arttırmakatdır. Aynı zamanda, SU bloğunun artık ağ ile birleştirilmesi geleneksel artık ağların performansını da arttırmaktadır. Önerilen yöntemin performansını test etmek için Kaggle veritabanından alınan BCCD veriseti kullanılmıştır. Uygulamalar sonucunda ortalama %99,92 doğruluk, %99,85 kesinlik, duyarlılık ve F1-skoru elde edilmiştir. Bu sonuçlar, literatürden BCCD verisetini kullanan son yıllardaki çalışmalarda yer alan ESA yöntemlerinin elde ettiği sonuçlarla karşılaştırıldı ve önerilen yöntemin daha az eğitilebilir parametre ile daha iyi sonuçlar verdiği görülmüştür.

Kaynakça

  • A. Girdhar, H. Kapur, and V. Kumar, “Classification of White blood cell using Convolution Neural Network”, Biomedical Signal Processing and Control, 71, 103156, 2022.
  • W. Stock and R. Hoffman, “White blood cells 1: Non-malignant disorders”, Lancet, 355, 1351–1357, 2000.
  • A. Girdhar, H. Kapur, V. Kumar, M. Kaur, D. Singh, and R. Damasevicius, “Effect of COVID-19 outbreak on urban health and environment”, Air Quality, Atmosphere & Health, 14(3), 389–397, 2021.
  • W. L. Tai, R. M. Hu, H. C. W. Hsiao, R. M. Chen, and J. J. P. Tsai, “Blood cell image classification based on hierarchical SVM”, IEEE International Symposium on Multimedia (ISM), California, USA, 129–136, 2011.
  • N. Ramesh, B. Dangott, M. E. Salama, and T. Tasdizen, “Isolation and two-step classification of normal white blood cells in peripheral blood smears”, Journal of Pathology Informatics, 3(1), 13, 2012.
  • S. Vatathanavaro, S. Tungjitnob, and K. Pasupa, “White Blood Cell Classification: A Comparison between VGG-16 and ResNet-50 Models”, 6th Joint Symposium on Computational Intelligence (JSCI6), Bangkok, Thailand, 2018.
  • A. M. Patil, M. D. Patil, and G. K. Birajdar, “White Blood Cells Image Classification Using Deep Learning with Canonical Correlation Analysis”, Irbm, 42(5), 378–389, 2021.
  • F. Long, J. J. Peng, W. Song, X. Xia, and J. Sang, “BloodCaps: A capsule network based model for the multiclassification of human peripheral blood cells”, Computer Methods and Programs in Biomedicine, 202, 2021.
  • X. Zheng, Y. Wang, G. Wang, and J. Liu, “Fast and robust segmentation of white blood cell images by self-supervised learning”, Micron, 107, 55–71, 2018.
  • D. M. U. Sabino, L. Da Fontoura Costa, E. G. Rizzatti, and M. A. Zago, “A texture approach to leukocyte recognition”, Real Time Imaging, 10(4), 205–216, 2004.
  • P. Ghosh, D. Bhattacharjee, and M. Nasipuri, “Blood smear analyzer for white blood cell counting: A hybrid microscopic image analyzing technique”, Applied Soft Computing Journal, 46, 629–638, 2016.
  • B. Dayı, H. Üzen, İ. B. Çiçek, and Ş. B. Duman, “A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs”, Diagnostics, 13(2), 202, 2023.
  • G. Liang, H. Hong, W. Xie, and L. Zheng, “Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification”, IEEE Access, 6, 36188–36197, 2018.
  • A. Ekiz, “ESA ve Kon-DVM Modelleri Kullanarak Beyaz Kan Hücrelerinin Sınıflandırılması”, 29th Signal Processing and Communications Applications Conference (SIU), İstanbul, Türkiye, 2021–2024, 9-11 June, 2021.
  • C. Cheuque, M. Querales, R. León, R. Salas, and R. Torres, “An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification”, Diagnostics, 12(2), 2022.
  • X. Yao, K. Sun, X. Bu, C. Zhao, and Y. Jin, “Classification of white blood cells using weighted optimized deformable convolutional neural networks”, Artificial. Nanomedicine Biotechnology, 49(1), 147–155, 2021. Cells,
  • A. Khan, A. Eker, A. Chefranov, and H. Demirel, “White blood cell type identification using multi-layer convolutional features with an extreme-learning machine”, Biomedical Signal Processing and Control, 69, 102932, 2021.
  • Y. Ha, Z. Du, and J. Tian, “Fine-grained interactive attention learning for semi-supervised white blood cell classification”, Biomedical Signal Processing and Control, 75, 103611, 2022.
  • N. Baghel, U. Verma, and K. K. Nagwanshi, “WBCs-Net: type identification of white blood cells using convolutional neural network”, Multimedia Tools and Applications, 81, 4213142147, 2021.
  • K. Balasubramanian, N. P. Ananthamoorthy, and K. Ramya, “An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm”, Neural Computing and Applications, 34(18), 16089–16101, 2022.
  • A. Sengur, Y. Akbulut, U. Budak, and Z. Comert, “White Blood Cell Classification Based on Shape and Deep Features”, 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), September,2019. Malatya, Türkiye, 21-22
  • A. Çınar and S. A. Tuncer, “Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM”, SN Applied Sciences, 3(4), 1–11, 2021.
  • M. A. R. Ridoy and M. R. Islam, “An automated approach to white blood cell classification using a lightweight convolutional neural network”, 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), Dhaka, Bangladesh, 480–483, 28-29 November, 2020.
  • F. Özyurt, “A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine”, Soft Computing, 24(11), 8163–8172, 2020.
  • M. Türkoğlu, K. Hanbay, I. S. Sivrikaya, and D. Hanbay, “Derin Evrişimsel Sinir Ağı Kullanılarak Kayısı Hastalıklarının Sınıflandırılması”, BEÜ Fen Bilimleri Dergisi, 9(1), 334–345, 2020.
  • H. Fırat, M. E. Asker, and D. Hanbay, “Depthwise Separable Convolution Based Residual Network Architecture for Hyperspectral Image Classification”, Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji, 10(2), 242–258, 2022.
  • J. Hu, L. Shen, and G. Sun, “Squeeze-and-Excitation Networks”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, USA, 7132–7141, 2018.
  • Y. Chen, Zhihao Zhang, and Lei Zhong, “Three-Stream Convolutional Neural Network with Squeeze-and-Excitation Block for Near-Infrared Facial Expression Recognition”, Electronics, 8(4), 385, 2019.
  • J. Wu et al., “WBC Image Segmentation Based on Residual Networks and Attentional Mechanisms”, Computational Intelligence and Neuroscience, 1610658, 2022.
  • Internet: P. Mooney, Kaggle - Blood Cell Images. www.kaggle.com/paultimothymooney/blood-cells (erişim tarihi: Jan. 30, 2022).
  • S. Nahzat, F. Bozkurt, and M. Yağanoğlu, “White Blood Cell Classification Using Convolutional Neural Network”, Journal of Science Technology and Engineering Research, 3(1), 32–41, 2022.
  • A. Khan, A. Eker, A. Chefranov, and H. Demirel, “White blood cell type identification using multi-layer convolutional features with an extreme-learning machine”, Biomedical Signal Processing and Control, 69, 102932, 2021.
  • M. Hosseini, D. Bani-Hani, and S. S. Lam, “Leukocytes Image Classification Using Optimized Convolutional Neural Networks”, Expert Systems with Applications, 205, 117672, 2022.
  • P. P. Banik, R. Saha, and K. D. Kim, “An Automatic Nucleus Segmentation and CNN Model based Classification Method of White Blood Cell”, Expert Systems with Applications, 149, 113211, 2020.
  • Y. Y. Baydilli and Ü. Atila, “Computerized Medical Imaging and Graphics Classification of white blood cells using capsule networks”, Computerized Medical Imaging and Graphics, 80, 2020.
  • P. P. Banik, R. Saha, and K. D. Kim, “Fused Convolutional Neural Network for White Blood Cell Image Classification”, 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Okinawa, Japan, 22–24, 11-13 Februray, 2019.
  • R. B. Hegde, K. Prasad, and H. Hebbar, “Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images”, Integrative Medicine Research, 39(2), 382–392, 2019.
  • S. Pang, A. Du, M. A. Orgun, and Z. Yu, “A novel fused convolutional neural network for biomedical image classification”, Medical & Biological Engineering & Computing, 57,107–121, 2019.
  • H. Kutlu, E. Avci, and F. Özyurt, “White blood cells detection and classi fi cation based on regional convolutional neural networks”, Medical Hypotheses, 135, 109472, 2020.

Classification of White Blood Cells using the Squeeze-Excitation Residual Network

Yıl 2023, , 189 - 205, 31.07.2023
https://doi.org/10.17671/gazibtd.1255477

Öz

White blood cells (WBCs) are an important component of the immune system that plays an active role in protecting the body against microorganisms such as parasites, bacteria and viruses. The structural features of WBCs, the shapes and numbers of their subtypes can provide important information about human health. Accurate WBC detection is clinically very important in the diagnosis of the disease. Accordingly, an accurate WBC classification method is of critical importance. In this study, a CNN-based method for WBC classification is proposed. The proposed method is a hybrid method consisting of a combination of squeeze-excitation (SE) network and residual network (ResNet) architecture. The problems that may occur as the number of layers increase in deep network architectures can be solved with ResNet. The use of the SE block with ResNet increases the classification accuracy while minimally increasing the total number of parameters. At the same time, combining the SE block with the ResNet improves the performance of traditional ResNets. The BCCD dataset from the Kaggle database was used to test the performance of the proposed method. As a result of the applications, an average of 99.92% accuracy, 99.85% precision, recall and F1-score were obtained. These results were compared with the results obtained by the CNN methods in recent studies using the BCCD dataset from the literature, and it was seen that the proposed method gave better results with less trainable parameters.

Kaynakça

  • A. Girdhar, H. Kapur, and V. Kumar, “Classification of White blood cell using Convolution Neural Network”, Biomedical Signal Processing and Control, 71, 103156, 2022.
  • W. Stock and R. Hoffman, “White blood cells 1: Non-malignant disorders”, Lancet, 355, 1351–1357, 2000.
  • A. Girdhar, H. Kapur, V. Kumar, M. Kaur, D. Singh, and R. Damasevicius, “Effect of COVID-19 outbreak on urban health and environment”, Air Quality, Atmosphere & Health, 14(3), 389–397, 2021.
  • W. L. Tai, R. M. Hu, H. C. W. Hsiao, R. M. Chen, and J. J. P. Tsai, “Blood cell image classification based on hierarchical SVM”, IEEE International Symposium on Multimedia (ISM), California, USA, 129–136, 2011.
  • N. Ramesh, B. Dangott, M. E. Salama, and T. Tasdizen, “Isolation and two-step classification of normal white blood cells in peripheral blood smears”, Journal of Pathology Informatics, 3(1), 13, 2012.
  • S. Vatathanavaro, S. Tungjitnob, and K. Pasupa, “White Blood Cell Classification: A Comparison between VGG-16 and ResNet-50 Models”, 6th Joint Symposium on Computational Intelligence (JSCI6), Bangkok, Thailand, 2018.
  • A. M. Patil, M. D. Patil, and G. K. Birajdar, “White Blood Cells Image Classification Using Deep Learning with Canonical Correlation Analysis”, Irbm, 42(5), 378–389, 2021.
  • F. Long, J. J. Peng, W. Song, X. Xia, and J. Sang, “BloodCaps: A capsule network based model for the multiclassification of human peripheral blood cells”, Computer Methods and Programs in Biomedicine, 202, 2021.
  • X. Zheng, Y. Wang, G. Wang, and J. Liu, “Fast and robust segmentation of white blood cell images by self-supervised learning”, Micron, 107, 55–71, 2018.
  • D. M. U. Sabino, L. Da Fontoura Costa, E. G. Rizzatti, and M. A. Zago, “A texture approach to leukocyte recognition”, Real Time Imaging, 10(4), 205–216, 2004.
  • P. Ghosh, D. Bhattacharjee, and M. Nasipuri, “Blood smear analyzer for white blood cell counting: A hybrid microscopic image analyzing technique”, Applied Soft Computing Journal, 46, 629–638, 2016.
  • B. Dayı, H. Üzen, İ. B. Çiçek, and Ş. B. Duman, “A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs”, Diagnostics, 13(2), 202, 2023.
  • G. Liang, H. Hong, W. Xie, and L. Zheng, “Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification”, IEEE Access, 6, 36188–36197, 2018.
  • A. Ekiz, “ESA ve Kon-DVM Modelleri Kullanarak Beyaz Kan Hücrelerinin Sınıflandırılması”, 29th Signal Processing and Communications Applications Conference (SIU), İstanbul, Türkiye, 2021–2024, 9-11 June, 2021.
  • C. Cheuque, M. Querales, R. León, R. Salas, and R. Torres, “An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification”, Diagnostics, 12(2), 2022.
  • X. Yao, K. Sun, X. Bu, C. Zhao, and Y. Jin, “Classification of white blood cells using weighted optimized deformable convolutional neural networks”, Artificial. Nanomedicine Biotechnology, 49(1), 147–155, 2021. Cells,
  • A. Khan, A. Eker, A. Chefranov, and H. Demirel, “White blood cell type identification using multi-layer convolutional features with an extreme-learning machine”, Biomedical Signal Processing and Control, 69, 102932, 2021.
  • Y. Ha, Z. Du, and J. Tian, “Fine-grained interactive attention learning for semi-supervised white blood cell classification”, Biomedical Signal Processing and Control, 75, 103611, 2022.
  • N. Baghel, U. Verma, and K. K. Nagwanshi, “WBCs-Net: type identification of white blood cells using convolutional neural network”, Multimedia Tools and Applications, 81, 4213142147, 2021.
  • K. Balasubramanian, N. P. Ananthamoorthy, and K. Ramya, “An approach to classify white blood cells using convolutional neural network optimized by particle swarm optimization algorithm”, Neural Computing and Applications, 34(18), 16089–16101, 2022.
  • A. Sengur, Y. Akbulut, U. Budak, and Z. Comert, “White Blood Cell Classification Based on Shape and Deep Features”, 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), September,2019. Malatya, Türkiye, 21-22
  • A. Çınar and S. A. Tuncer, “Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM”, SN Applied Sciences, 3(4), 1–11, 2021.
  • M. A. R. Ridoy and M. R. Islam, “An automated approach to white blood cell classification using a lightweight convolutional neural network”, 2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT), Dhaka, Bangladesh, 480–483, 28-29 November, 2020.
  • F. Özyurt, “A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine”, Soft Computing, 24(11), 8163–8172, 2020.
  • M. Türkoğlu, K. Hanbay, I. S. Sivrikaya, and D. Hanbay, “Derin Evrişimsel Sinir Ağı Kullanılarak Kayısı Hastalıklarının Sınıflandırılması”, BEÜ Fen Bilimleri Dergisi, 9(1), 334–345, 2020.
  • H. Fırat, M. E. Asker, and D. Hanbay, “Depthwise Separable Convolution Based Residual Network Architecture for Hyperspectral Image Classification”, Gazi Üniversitesi Fen Bilimleri Dergisi Part C Tasarım ve Teknoloji, 10(2), 242–258, 2022.
  • J. Hu, L. Shen, and G. Sun, “Squeeze-and-Excitation Networks”, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, USA, 7132–7141, 2018.
  • Y. Chen, Zhihao Zhang, and Lei Zhong, “Three-Stream Convolutional Neural Network with Squeeze-and-Excitation Block for Near-Infrared Facial Expression Recognition”, Electronics, 8(4), 385, 2019.
  • J. Wu et al., “WBC Image Segmentation Based on Residual Networks and Attentional Mechanisms”, Computational Intelligence and Neuroscience, 1610658, 2022.
  • Internet: P. Mooney, Kaggle - Blood Cell Images. www.kaggle.com/paultimothymooney/blood-cells (erişim tarihi: Jan. 30, 2022).
  • S. Nahzat, F. Bozkurt, and M. Yağanoğlu, “White Blood Cell Classification Using Convolutional Neural Network”, Journal of Science Technology and Engineering Research, 3(1), 32–41, 2022.
  • A. Khan, A. Eker, A. Chefranov, and H. Demirel, “White blood cell type identification using multi-layer convolutional features with an extreme-learning machine”, Biomedical Signal Processing and Control, 69, 102932, 2021.
  • M. Hosseini, D. Bani-Hani, and S. S. Lam, “Leukocytes Image Classification Using Optimized Convolutional Neural Networks”, Expert Systems with Applications, 205, 117672, 2022.
  • P. P. Banik, R. Saha, and K. D. Kim, “An Automatic Nucleus Segmentation and CNN Model based Classification Method of White Blood Cell”, Expert Systems with Applications, 149, 113211, 2020.
  • Y. Y. Baydilli and Ü. Atila, “Computerized Medical Imaging and Graphics Classification of white blood cells using capsule networks”, Computerized Medical Imaging and Graphics, 80, 2020.
  • P. P. Banik, R. Saha, and K. D. Kim, “Fused Convolutional Neural Network for White Blood Cell Image Classification”, 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Okinawa, Japan, 22–24, 11-13 Februray, 2019.
  • R. B. Hegde, K. Prasad, and H. Hebbar, “Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images”, Integrative Medicine Research, 39(2), 382–392, 2019.
  • S. Pang, A. Du, M. A. Orgun, and Z. Yu, “A novel fused convolutional neural network for biomedical image classification”, Medical & Biological Engineering & Computing, 57,107–121, 2019.
  • H. Kutlu, E. Avci, and F. Özyurt, “White blood cells detection and classi fi cation based on regional convolutional neural networks”, Medical Hypotheses, 135, 109472, 2020.
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Hüseyin Fırat 0000-0002-1257-8518

Yayımlanma Tarihi 31 Temmuz 2023
Gönderilme Tarihi 23 Şubat 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Fırat, H. (2023). Sıkma-Uyarma Artık Ağı kullanılarak Beyaz Kan Hücrelerinin Sınıflandırılması. Bilişim Teknolojileri Dergisi, 16(3), 189-205. https://doi.org/10.17671/gazibtd.1255477