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Modifiye Edilmiş Inception Modülü Kullanılarak İnsan Periferik Kan Hücrelerinin Çoklu Sınıflandırılması

Year 2023, Volume: 5 Issue: 2, 272 - 284, 27.10.2023

Hüseyin Fırat

https://doi.org/10.46387/bjesr.1302685

Abstract

Periferik kan hücrelerinin sınıflandırılması anemi ve lösemi gibi birçok kan hastalığının teşhisinde önemli rol oynamaktadır. Bu nedenle, doğru kan hücresi sınıflandırması hastalığın teşhisinde klinik olarak oldukça önemlidir. Son yıllarda, derin öğrenme, özellikle Evrişimsel sinir ağları, güçlü kendi kendine öğrenme yetenekleri sayesinde tıp alanında sıklıkla kullanılmaktadır. Bu çalışmada, kan hücre sınıflandırması için hesaplama maliyetini ve parametre sayısını azaltan derinlemesine ayrılabilir evrişim ile Inception modülünden oluşan yeni bir hibrit yöntem geliştirilmiştir. Bu yöntem, parametre sayısını ve hesaplama maliyetini azaltıp sınıflandırma doğruluğunu arttırmasıyla, standart evrişimsel sinir ağlarına göre bir avantaj sağlamaktadır. Geliştirilen yöntemin performansını test etmek için 8 sınıflı bir kan hücresi veri seti üzerinde yapılan deneysel çalışmalar sonucunda %98.89 doğruluk, %98.88 kesinlik, %98.85 duyarlılık, %98.86 F1-skoru elde edilmiştir. Literatürdeki çalışmalar ile karşılaştırıldığında yöntemimizin etkili olduğu görülmektedir.

Keywords

Periferik Kan Hücresi Sınıflandırma Inception modülü Derinlemesine Ayrılabilir Evrişim

References

  • Y.Y. Baydilli and Ü. Atila “Classification of white blood cells using capsule networks,” Comput. Med. Imaging Graph., vol. 80, 2020.
  • R.B. Hegde, K. Prasad, H. Hebbar, and B.M.K. Singh “Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images,” Biocybern. Biomed. Eng., vol. 39, no. 2, pp. 382–392, 2019.
  • 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,” Comput. Methods Programs Biomed., vol. 202, no. 105972, 2021.
  • A. Khan, A. Eker, A. Chefranov, and H. Demirel “White blood cell type identification using multi-layer convolutional features with an extreme-learning machine,” Biomed. Signal Process. Control, vol. 69, no. November 2020, p. 102932, 2021.
  • G. Liang, H. Hong, W. Xie, and L. Zheng “Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification,” IEEE Access, vol. 6, pp. 36188–36197, 2018.
  • N. Bayat, D.D. Davey, M. Coathup, and J.-H. Park “White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization,” Big Data Cogn. Comput., vol. 6, no. 4, p. 122, 2022.
  • N. Dong, M. die Zhai, J. fang Chang, and C. ho Wu “A self-adaptive approach for white blood cell classification towards point-of-care testing,” Appl. Soft Comput., vol. 111, p. 107709, 2021.
  • N. Ramesh, B. Dangott, M.E. Salama, and T. Tasdizen “Isolation and two-step classification of normal white blood cells in peripheral blood smears,” J. Pathol. Inform., vol. 3, no. 1, p. 13, 2012.
  • X. Yao, K. Sun, X. Bu, C. Zhao, and Y. Jin “Classification of white blood cells using weighted optimized deformable convolutional neural networks,” Artif. Cells, Nanomedicine Biotechnol., vol. 49, no. 1, pp. 147–155, 2021.
  • A. Girdhar, H. Kapur, and V. Kumar “Classification of White blood cell using Convolution Neural Network,” Biomed. Signal Process. Control, vol. 71, no. PA, p. 103156, 2022.
  • A. Çınar and S.A. Tuncer “Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM,” SN Appl. Sci., vol. 3, no. 4, pp. 1–11, 2021.
  • R.B. Hegde, K. Prasad, H. Hebbar, B.M.K. Singh, and I. Sandhya “Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images,” J. Digit. Imaging, vol. 33, no. 2, pp. 361–374, 2020.
  • J. Prinyakupt and C. Pluempitiwiriyawej “Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers,” Biomed. Eng. Online, vol. 14, no. 1, pp. 1–19, 2015.
  • J. Zhao, M. Zhang, Z. Zhou, J. Chu, and F. Cao “Automatic detection and classification of leukocytes using convolutional neural networks,” Med. Biol. Eng. Comput., vol. 55, no. 8, pp. 1287–1301, 2017.
  • 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, vol. 13, no. 2, p. 202, 2023.
  • E. Bütün, M. Uçan, and M. Kaya “Automatic detection of cancer metastasis in lymph node using deep learning,” Biomed. Signal Process. Control, vol. 82, no. August 2022, p. 104564, 2023.
  • M. Toğaçar, B. Ergen, and Z. Cömert “Classification of white blood cells using deep features obtained from Convolutional Neural Network models based on the combination of feature selection methods,” Appl. Soft Comput. J., vol. 97, p. 106810, 2020.
  • F. Uçar “Deep Learning Approach to Cell Classification in Human Peripheral Blood,” in 2020 5th International Conference on Computer Science and Engineering (UBMK), 2020, pp. 383–387.
  • A. Sharma, S. C. Thomas, A. Sah, V. V. Abhyankar, V. K. Singh, and S. Prakash “White Blood Cells Subtypes Classification Using Fast Traditional Convolutional Neural Network,” Proc. 2021 Int.Conf. Emerg. Tech. Comput. Intell. ICETCI 2021 , pp. 113–117, 2021.
  • D. Baby, S.J. Devaraj, J. Hemanth, and M.M. Anishin Raj “Leukocyte classification based on feature selection using extra trees classifier: A transfer learning approach,” Turkish J. Electr. Eng. Comput. Sci., vol. 29, no. 8, pp. 2742–2757, 2021.
  • D. Bani-Hani, N. Khan, F. Alsultan, S. Karanjkar, and N. Nagarur “Classification of Leucocytes Using Convolutional Neural Network Optimized Through Genetic Algorithm,” Proc. 7th Annu. World Conf. Soc. Ind. Syst. Eng. Binghamton, NY, USA, vol. 10, no. November, pp. 1–7, 2018.
  • E.H. Mohamed, W.H. El-Behaidy, G. Khoriba, and J. Li “Improved white blood cells classification based on pre-trained deep learning models,” J. Commun. Softw. Syst., vol. 16, no. 1, pp. 37–45, 2020.
  • A.M. Patil, M.D. Patil, and G.K. Birajdar “White Blood Cells Image Classification Using Deep Learning with Canonical Correlation Analysis,” Irbm, vol. 42, no. 5, pp. 378–389, 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, vol. 12, no. 2, 2022.
  • P.P. Banik, R. Saha, and K.D. Kim “Fused Convolutional Neural Network for White Blood Cell Image Classification,” 2019 Int. Conf. Artif. Intell. Inf. Commun., pp. 22–24, 2019.
  • A. Acevedo, A. Merino, S. Alférez, Á. Molina, L. Boldú, and J. Rodellar, “A dataset of microscopic peripheral blood cell images for development of automatic recognition systems,” Data Br., vol. 30, p. 105474, 2020.
  • C. Szegedy et al. “Going deeper with convolutions,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–9, 2015.
  • H. Fırat, M. Emin, A. Mehmet, I. Bayındır, and D. Hanbay “Hybrid 3D / 2D Complete Inception Module and Convolutional Neural Network for Hyperspectral Remote Sensing Image Classification,” Neural Process. Lett., pp. 1–44, 2022.
  • F. Chollet “Xception: Deep learning with depthwise separable convolutions,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017 , vol. 2017-Janua, pp. 1800–1807, 2017.
  • A.G. Howard et al. “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” 2017.
  • H. Üzen, M. Turkoglu, M. Aslan, and D. Hanbay “Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection,” Vis. Comput., 2022.
  • H. Fırat, M. E. Asker, and D. Hanbay “Hybrid 3D Convolution and 2D Depthwise Separable Convolution Neural Network for Hyperspectral Image Classification,” Balk. J. Electr. Comput. Eng., vol. 10, no. 1, pp. 35–46, 2022.
  • A. Tuncer “Cost-optimized hybrid convolutional neural networks for detection of plant leaf diseases,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 8, pp. 8625–8636, 2021.
  • Y. Ha, Z. Du, and J. Tian “Fine-grained interactive attention learning for semi-supervised white blood cell classification,” Biomed. Signal Process. Control, vol. 75, no. September 2021, p. 103611, 2022.
  • A.I. Shahin, Y. Guo, K.M. Amin, and A.A. Sharawi “White blood cells identification system based on convolutional deep neural learning networks,” Comput. Methods Programs Biomed., vol. 168, pp. 69–80, 2019.
  • A. Naseri and A. Rezaei Nasab “Automatic identification of minerals in thin sections using image processing,” J. Ambient Intell. Humaniz. Comput., no. 0123456789, 2021.
  • A. Acevedo, S. Alférez, A. Merino, L. Puigví, and J. Rodellar “Recognition of peripheral blood cell images using convolutional neural networks,” Comput. Methods Programs Biomed., vol. 180, p. 105020, 2019.
  • C. Di Ruberto, A. Loddo, and L. Putzu “Detection of red and white blood cells from microscopic blood images using a region proposal approach,” Comput. Biol. Med., vol. 116, no. August 2019, p. 103530, 2020.
  • H. Atıcı and H.E. Koçer “Mask R-CNN Based Segmentation and Classification of Blood Smear Images,” Gazi J. Eng. Sci., vol. 9, no. 1, pp. 128–143, 2023.

Multiple Classification of Human Peripheral Blood Cells Using Modified Inception Module

Year 2023, Volume: 5 Issue: 2, 272 - 284, 27.10.2023

Hüseyin Fırat

https://doi.org/10.46387/bjesr.1302685

Abstract

Classification of peripheral blood cells plays an important role in the diagnosis of many blood diseases such as anemia, leukemia etc. Therefore, correct blood cell classification is clinically very significant in diagnosing the disease. In recent years, deep learning, especially Convolutional neural networks, has been used frequently in the medical field thanks to its strong self-learning capabilities. In this study, a new hybrid method consisting of depthwise separable convolution and Inception module has been developed, which reduces the computational cost and the number of parameters for blood cell classification. This method provides an advantage over standard convolutional neural networks by reducing the number of parameters and computational cost and increasing the classification accuracy. As a result of experimental studies conducted on an 8-class blood cell dataset to test the performance of the developed method, 98.89% accuracy, 98.88% accuracy, 98.85% sensitivity, 98.86% F1-score were obtained. It is seen that our method is effective when compared with the studies in the literature.

Keywords

Peripheral Blood Cell Classification Inception Module Depthwise Separable Convolution

References

  • Y.Y. Baydilli and Ü. Atila “Classification of white blood cells using capsule networks,” Comput. Med. Imaging Graph., vol. 80, 2020.
  • R.B. Hegde, K. Prasad, H. Hebbar, and B.M.K. Singh “Comparison of traditional image processing and deep learning approaches for classification of white blood cells in peripheral blood smear images,” Biocybern. Biomed. Eng., vol. 39, no. 2, pp. 382–392, 2019.
  • 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,” Comput. Methods Programs Biomed., vol. 202, no. 105972, 2021.
  • A. Khan, A. Eker, A. Chefranov, and H. Demirel “White blood cell type identification using multi-layer convolutional features with an extreme-learning machine,” Biomed. Signal Process. Control, vol. 69, no. November 2020, p. 102932, 2021.
  • G. Liang, H. Hong, W. Xie, and L. Zheng “Combining Convolutional Neural Network With Recursive Neural Network for Blood Cell Image Classification,” IEEE Access, vol. 6, pp. 36188–36197, 2018.
  • N. Bayat, D.D. Davey, M. Coathup, and J.-H. Park “White Blood Cell Classification Using Multi-Attention Data Augmentation and Regularization,” Big Data Cogn. Comput., vol. 6, no. 4, p. 122, 2022.
  • N. Dong, M. die Zhai, J. fang Chang, and C. ho Wu “A self-adaptive approach for white blood cell classification towards point-of-care testing,” Appl. Soft Comput., vol. 111, p. 107709, 2021.
  • N. Ramesh, B. Dangott, M.E. Salama, and T. Tasdizen “Isolation and two-step classification of normal white blood cells in peripheral blood smears,” J. Pathol. Inform., vol. 3, no. 1, p. 13, 2012.
  • X. Yao, K. Sun, X. Bu, C. Zhao, and Y. Jin “Classification of white blood cells using weighted optimized deformable convolutional neural networks,” Artif. Cells, Nanomedicine Biotechnol., vol. 49, no. 1, pp. 147–155, 2021.
  • A. Girdhar, H. Kapur, and V. Kumar “Classification of White blood cell using Convolution Neural Network,” Biomed. Signal Process. Control, vol. 71, no. PA, p. 103156, 2022.
  • A. Çınar and S.A. Tuncer “Classification of lymphocytes, monocytes, eosinophils, and neutrophils on white blood cells using hybrid Alexnet-GoogleNet-SVM,” SN Appl. Sci., vol. 3, no. 4, pp. 1–11, 2021.
  • R.B. Hegde, K. Prasad, H. Hebbar, B.M.K. Singh, and I. Sandhya “Automated Decision Support System for Detection of Leukemia from Peripheral Blood Smear Images,” J. Digit. Imaging, vol. 33, no. 2, pp. 361–374, 2020.
  • J. Prinyakupt and C. Pluempitiwiriyawej “Segmentation of white blood cells and comparison of cell morphology by linear and naïve Bayes classifiers,” Biomed. Eng. Online, vol. 14, no. 1, pp. 1–19, 2015.
  • J. Zhao, M. Zhang, Z. Zhou, J. Chu, and F. Cao “Automatic detection and classification of leukocytes using convolutional neural networks,” Med. Biol. Eng. Comput., vol. 55, no. 8, pp. 1287–1301, 2017.
  • 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, vol. 13, no. 2, p. 202, 2023.
  • E. Bütün, M. Uçan, and M. Kaya “Automatic detection of cancer metastasis in lymph node using deep learning,” Biomed. Signal Process. Control, vol. 82, no. August 2022, p. 104564, 2023.
  • M. Toğaçar, B. Ergen, and Z. Cömert “Classification of white blood cells using deep features obtained from Convolutional Neural Network models based on the combination of feature selection methods,” Appl. Soft Comput. J., vol. 97, p. 106810, 2020.
  • F. Uçar “Deep Learning Approach to Cell Classification in Human Peripheral Blood,” in 2020 5th International Conference on Computer Science and Engineering (UBMK), 2020, pp. 383–387.
  • A. Sharma, S. C. Thomas, A. Sah, V. V. Abhyankar, V. K. Singh, and S. Prakash “White Blood Cells Subtypes Classification Using Fast Traditional Convolutional Neural Network,” Proc. 2021 Int.Conf. Emerg. Tech. Comput. Intell. ICETCI 2021 , pp. 113–117, 2021.
  • D. Baby, S.J. Devaraj, J. Hemanth, and M.M. Anishin Raj “Leukocyte classification based on feature selection using extra trees classifier: A transfer learning approach,” Turkish J. Electr. Eng. Comput. Sci., vol. 29, no. 8, pp. 2742–2757, 2021.
  • D. Bani-Hani, N. Khan, F. Alsultan, S. Karanjkar, and N. Nagarur “Classification of Leucocytes Using Convolutional Neural Network Optimized Through Genetic Algorithm,” Proc. 7th Annu. World Conf. Soc. Ind. Syst. Eng. Binghamton, NY, USA, vol. 10, no. November, pp. 1–7, 2018.
  • E.H. Mohamed, W.H. El-Behaidy, G. Khoriba, and J. Li “Improved white blood cells classification based on pre-trained deep learning models,” J. Commun. Softw. Syst., vol. 16, no. 1, pp. 37–45, 2020.
  • A.M. Patil, M.D. Patil, and G.K. Birajdar “White Blood Cells Image Classification Using Deep Learning with Canonical Correlation Analysis,” Irbm, vol. 42, no. 5, pp. 378–389, 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, vol. 12, no. 2, 2022.
  • P.P. Banik, R. Saha, and K.D. Kim “Fused Convolutional Neural Network for White Blood Cell Image Classification,” 2019 Int. Conf. Artif. Intell. Inf. Commun., pp. 22–24, 2019.
  • A. Acevedo, A. Merino, S. Alférez, Á. Molina, L. Boldú, and J. Rodellar, “A dataset of microscopic peripheral blood cell images for development of automatic recognition systems,” Data Br., vol. 30, p. 105474, 2020.
  • C. Szegedy et al. “Going deeper with convolutions,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1–9, 2015.
  • H. Fırat, M. Emin, A. Mehmet, I. Bayındır, and D. Hanbay “Hybrid 3D / 2D Complete Inception Module and Convolutional Neural Network for Hyperspectral Remote Sensing Image Classification,” Neural Process. Lett., pp. 1–44, 2022.
  • F. Chollet “Xception: Deep learning with depthwise separable convolutions,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017 , vol. 2017-Janua, pp. 1800–1807, 2017.
  • A.G. Howard et al. “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” 2017.
  • H. Üzen, M. Turkoglu, M. Aslan, and D. Hanbay “Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection,” Vis. Comput., 2022.
  • H. Fırat, M. E. Asker, and D. Hanbay “Hybrid 3D Convolution and 2D Depthwise Separable Convolution Neural Network for Hyperspectral Image Classification,” Balk. J. Electr. Comput. Eng., vol. 10, no. 1, pp. 35–46, 2022.
  • A. Tuncer “Cost-optimized hybrid convolutional neural networks for detection of plant leaf diseases,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 8, pp. 8625–8636, 2021.
  • Y. Ha, Z. Du, and J. Tian “Fine-grained interactive attention learning for semi-supervised white blood cell classification,” Biomed. Signal Process. Control, vol. 75, no. September 2021, p. 103611, 2022.
  • A.I. Shahin, Y. Guo, K.M. Amin, and A.A. Sharawi “White blood cells identification system based on convolutional deep neural learning networks,” Comput. Methods Programs Biomed., vol. 168, pp. 69–80, 2019.
  • A. Naseri and A. Rezaei Nasab “Automatic identification of minerals in thin sections using image processing,” J. Ambient Intell. Humaniz. Comput., no. 0123456789, 2021.
  • A. Acevedo, S. Alférez, A. Merino, L. Puigví, and J. Rodellar “Recognition of peripheral blood cell images using convolutional neural networks,” Comput. Methods Programs Biomed., vol. 180, p. 105020, 2019.
  • C. Di Ruberto, A. Loddo, and L. Putzu “Detection of red and white blood cells from microscopic blood images using a region proposal approach,” Comput. Biol. Med., vol. 116, no. August 2019, p. 103530, 2020.
  • H. Atıcı and H.E. Koçer “Mask R-CNN Based Segmentation and Classification of Blood Smear Images,” Gazi J. Eng. Sci., vol. 9, no. 1, pp. 128–143, 2023.
There are 39 citations in total.

Details

Primary Language Turkish
Subjects Artificial Intelligence
Journal Section Research Articles
Authors

Hüseyin Fırat 0000-0002-1257-8518

Early Pub Date October 18, 2023
Publication Date October 27, 2023
Published in Issue Year 2023 Volume: 5 Issue: 2

Cite

APA Fırat, H. (2023). Modifiye Edilmiş Inception Modülü Kullanılarak İnsan Periferik Kan Hücrelerinin Çoklu Sınıflandırılması. Mühendislik Bilimleri Ve Araştırmaları Dergisi, 5(2), 272-284. https://doi.org/10.46387/bjesr.1302685
AMA Fırat H. Modifiye Edilmiş Inception Modülü Kullanılarak İnsan Periferik Kan Hücrelerinin Çoklu Sınıflandırılması. BJESR. October 2023;5(2):272-284. doi:10.46387/bjesr.1302685
Chicago Fırat, Hüseyin. “Modifiye Edilmiş Inception Modülü Kullanılarak İnsan Periferik Kan Hücrelerinin Çoklu Sınıflandırılması”. Mühendislik Bilimleri Ve Araştırmaları Dergisi 5, no. 2 (October 2023): 272-84. https://doi.org/10.46387/bjesr.1302685.
EndNote Fırat H (October 1, 2023) Modifiye Edilmiş Inception Modülü Kullanılarak İnsan Periferik Kan Hücrelerinin Çoklu Sınıflandırılması. Mühendislik Bilimleri ve Araştırmaları Dergisi 5 2 272–284.
IEEE H. Fırat, “Modifiye Edilmiş Inception Modülü Kullanılarak İnsan Periferik Kan Hücrelerinin Çoklu Sınıflandırılması”, BJESR, vol. 5, no. 2, pp. 272–284, 2023, doi: 10.46387/bjesr.1302685.
ISNAD Fırat, Hüseyin. “Modifiye Edilmiş Inception Modülü Kullanılarak İnsan Periferik Kan Hücrelerinin Çoklu Sınıflandırılması”. Mühendislik Bilimleri ve Araştırmaları Dergisi 5/2 (October 2023), 272-284. https://doi.org/10.46387/bjesr.1302685.
JAMA Fırat H. Modifiye Edilmiş Inception Modülü Kullanılarak İnsan Periferik Kan Hücrelerinin Çoklu Sınıflandırılması. BJESR. 2023;5:272–284.
MLA Fırat, Hüseyin. “Modifiye Edilmiş Inception Modülü Kullanılarak İnsan Periferik Kan Hücrelerinin Çoklu Sınıflandırılması”. Mühendislik Bilimleri Ve Araştırmaları Dergisi, vol. 5, no. 2, 2023, pp. 272-84, doi:10.46387/bjesr.1302685.
Vancouver Fırat H. Modifiye Edilmiş Inception Modülü Kullanılarak İnsan Periferik Kan Hücrelerinin Çoklu Sınıflandırılması. BJESR. 2023;5(2):272-84.
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