Automatic Leukocytes Classification using Deep Convolutional Neural Network

SUGONDO HADIYOSO, SUCI AULIA

Sari


ABSTRAK

Sel darah putih atau leukosit adalah salah satu bagian darah yang bertanggung jawab untuk sistem kekebalan tubuh. Penghitungan setiap jenis leukosit merupakan hal yang krusial untuk menentukan status kesehatan. Sel darah dihitung menggunakan hematology analyzer. Namun, perangkat ini hanya tersedia di laboratorium klinik pusat atau rumah sakit. Saat ini masih banyak clinician yang melakukan perhitungan manual dengan memperkirakan jumlah leukosit menggunakan mikroskop. Hal ini berpotensi menimbulkan kesalahan perhitungan yang tinggi. Oleh karena itu, penelitian ini mengusulkan suatu sistem yang dapat mengklasifikasikan jenis-jenis leukosit. Metode convolutional neural network (CNN) dengan arsitektur VGG-19 digunakan dalam klasifikasi leukosit. Beberapa skenario pengujian dengan mengubah parameter epoch dan ukuran batch diterapkan untuk mendapatkan akurasi terbaik. Hasil simulasi model pembelajaran yang digunakan dapat menghasilkan akurasi hingga 100% untuk mengklasifikasikan neutrofil, eosinofil, monosit, dan limfosit. Hasil ini dicapai dengan menggunakan pengoptimal Adam, Epoch=5 dan batch size=60.

Kata kunci: leukosit, klasifikasi, CNN, VGG-16

 

ABSTRACT

White blood cells or leukocytes are one of the blood components responsible for the body's immune system. Counting each type of leukocyte is a crucial thing to determine the health status. Blood cells were counted using a hematology analyzer. However, this device is only available in central clinical laboratories or hospitals. Currently, there are still many clinicians doing manual calculations by estimating the number of leukocytes using a microscope. This has the potential to generate high errors in calculations. Therefore, this study proposes a system that can classify the types of leukocytes. The convolutional neural network (CNN) method with VGG-19 architecture was employed in leukocyte classification. Several test scenarios by changing the epoch and batch size parameters were applied to obtain the best accuracy. The results of the simulation of the learning model used can generate accuracy up to 100% for classifying neutrophils, eosinophils, monocytes, and lymphocytes. This result was achieved using Adam optimizer, epoch=5 and batch size=60.

Keywords: leukocyte, classification, CNN, VGG-16


Kata Kunci


leukocyte; classification; CNN; VGG-16

Teks Lengkap:

PDF (English)

Referensi


Abou El-Seoud, M. S., Siala, M. H., & McKee, G. (2020). Detection and Classification of White Blood Cells Through Deep Learning Techniques. International Journal of Online and Biomedical Engineering, 16(15), 94–105. https://doi.org/10.3991/ijoe.v16i15.15481

Al-Dulaimi, K.A.K., Banks, J., Chandran, V., Tomeo-Reyes, I. and Nguyen Thanh, K. (2018). Classification of White Blood Cell Types from Microscope Images: Techniques and Challenges. In Microscopy Science: Last Approaches on Educational Programs and Applied Research (Vol. 8, Issue November).

Ali, M., & Ali, R. (2021). Multi-input dual-stream capsule network for improved lung and colon cancer classification. Diagnostics, 11(8), 1–18. https://doi.org/10.3390/diagnostics11081485

Aulia, S., Hadiyoso, S., Latifah, T., & Suksmono, A. B. (2021). Covid-19 and Tuberculosis Classification Based on Chest X-Ray Using Convolutional Neural Network. Lecture Note in Electrical Engingeering, 746(1), 407–420.

Bendarkar, D., Somase, P., Rebari, P., Paturkar, R., & Khan, A. (2021). Web Based Recognition and Translation of American Sign Language with CNN and RNN. International Journal of Online and Biomedical Engineering, 17(1), 34–50. https://doi.org/10.3991/ijoe.v17i01.18585

Beyeler, M. (2017). Machine learning for OpenCV : a practical introduction to the world of machine learning and image processing using OpenCV and Python (Issue june). Packt Publishing Ltd.

Elnakib, A., Amer, H. M., & Abou-Chadi, F. E. Z. (2020). Early lung cancer detection using deep learning optimization. International Journal of Online and Biomedical Engineering, 16(6), 82–94. https://doi.org/10.3991/ijoe.v16i06.13657

Falcón-ruiz, A., Taboada-crispí, A., & Orozco-monteagudo, M. (2010). Classification of White Blood Cells Using Morphometric Features of Nucleus. Cuba-Flanders Workshop on Machine Learning and Knowledge Discovery, 1–9.

Farley, A., Hendry, C., & McLafferty, E. (2012). Blood components. Nursing Standard, 27(13), 35–42. https://doi.org/10.7748/ns2012.11.27.13.35.c9449

Fitri, Z. E., Syahputri, L. N. Y., & Imron, A. M. N. (2020). Classification of White Blood Cell Abnormalities for Early Detection of Myeloproliferative Neoplasms Syndrome Based on KNearest Neighborr. Scientific Journal of Informatics, 7(1), 136–142. https://doi.org/10.15294/sji.v7i1.24372

Gavet, Y., & Debayle, J. (2019). Image processing tutorials with python. Spartacus.

Hodneland, E., Bukoreshtliev, N. V., Eichler, T. W., Tai, X. C., Gurke, S., Lundervold, A., & Gerdes, H. H. (2009). A unified framework for automated 3-D segmentation of surfacestained living cells and a comprehensive segmentation evaluation. IEEE Transactions on Medical Imaging, 28(5), 720–738. https://doi.org/10.1109/TMI.2008.2011522

Kuan, D. H., Wu, C. C., Su, W. Y., & Huang, N. T. (2018). A Microfluidic Device for Simultaneous Extraction of Plasma, Red Blood Cells, and On-Chip White Blood Cell Trapping. Scientific Reports, 8(1), 1–9. https://doi.org/10.1038/s41598-018-33738-8

Marenzana, M., & Arnett, T. R. (2013). The Key Role of the Blood Supply to Bone. Bone Research, 1, 203–215. https://doi.org/10.4248/BR201303001

Maysanjaya, I. M. D. (2020). Klasifikasi Pneumonia pada Citra X-rays Paru-paru dengan Convolutional Neural Network. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 9(2), 190–195. https://doi.org/10.22146/jnteti.v9i2.66

Mourtada, B., XAVIER, D., MOHAMMED AMINE, C., & MOHAMMED LAMINE, B. (2019). Multi Features Based Approach for White Blood Cells Segmentation and Classification in Peripheral Blood and Bone Marrow Images. International Journal of Biomedical Engineering and Technology, 1(1), 1–19. https://doi.org/10.1504/ijbet.2019.10030162

Nanditha, B. R., Kiran, A. G., Chandrashekar, H. S., Dinesh, M. S., & Murali, S. (2021). An Ensemble Deep Neural Network Approach for Oral Cancer Screening. International Journal of Online and Biomedical Engineering, 17(2), 121–134. https://doi.org/10.3991/ijoe.v17i02.19207

Nicholson, L. B. (2016). The immune system. Essays in Biochemistry, 60(3), 275–301. https://doi.org/10.1042/EBC20160017

Putzu, L., & Ruberto, C. Di. (2013). White Blood Cells Identification and Classification from Leukemic Blood Image. Proceedings of the IWBBIO International Work …, 7(January), 18–20. http://iwbbio.ugr.es/papers/iwbbio_015.pdf

Reyes-cadena, S. (2014). Leukocytes Detection, Classification and Counting in Smears of Peripheral Blood. Revista Mexicana de Ingeniería Biomédica, 35(1), 41–51.

Samreen, A., Taha, A. M., Reddy, Y. V., & Sathish, P. (2020). Brain Tumor Detection by Using Convolution Neural Network. International Journal of Online and Biomedical Engineering, 16(13), 58–69. https://doi.org/10.3991/ijoe.v16i13.18545

Sunario Megawan, & Wulan Sri Lestari. (2020). Deteksi Spoofing Wajah Menggunakan Faster R-CNN dengan Arsitektur Resnet50 pada Video. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 9(3), 261–267. https://doi.org/10.22146/.v9i3.231

Tsihrintzis, G. A., Virvou, M., Sakkopoulos, E., & Jain, L. C. (2018). Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems (L. C. J. George A. Tsihrintzis, Maria Virvou, Evangelos Sakkopoulos (ed.)). Springer Cham.

Weber, M., Steinle, H., Golombek, S., Hann, L., Schlensak, C., Wendel, H. P., & Avci-Adali, M. (2018). Blood-Contacting Biomaterials: In Vitro Evaluation of the Hemocompatibility. Frontiers in Bioengineering and Biotechnology, 6(July), 1–11. https://doi.org/10.3389/fbioe.2018.00099

Widhiyasana, Y., Semiawan, T., Gibran, I., Mudzakir, A., & Noor, M. R. (2021). Penerapan Convolutional Long Short-Term Memory untuk Klasifikasi Teks Berita Bahasa Indonesia. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 10(4), 354–361.

Zhang, Q., Zhang, M., Chen, T., Sun, Z., Ma, Y., & Yu, B. (2019). Recent advances in convolutional neural network acceleration. Neurocomputing, 323, 37–51. https://doi.org/10.1016/j.neucom.2018.09.038




DOI: https://doi.org/10.26760/elkomika.v11i1.195

Refbacks

  • Saat ini tidak ada refbacks.


_______________________________________________________________________________________________________________________

ISSN (cetak) : 2338-8323 | ISSN (elektronik) : 2459-9638

diterbitkan oleh :

Teknik Elektro Institut Teknologi Nasional Bandung

Alamat : Gedung 20 Jl. PHH. Mustofa 23 Bandung 40124

Kontak : Tel. 7272215 (ext. 206) Fax. 7202892

Surat Elektronik : jte.itenas@itenas.ac.id________________________________________________________________________________________________________________________

Statistik Pengunjung

Free counters!

Web Analytics Made Easy - StatCounter

Lihat Statistik Jurnal

Jurnal ini terlisensi oleh Creative Commons Attribution-ShareAlike 4.0 International License.

Creative Commons License