Penerapan Arsitektur EfficientNet-B0 Pada Klasifikasi Leukimia Tipe Acute Lymphoblastik Leukimia

Alfataniah Nur Fajrina; Zein Hanni Pradana; Sevia Indah Purnama; Shinta Romadhona

doi:10.30595/jrre.v6i1.22090

Authors

Alfataniah Nur Fajrina Institut Teknologi Telkom Purwokerto
Zein Hanni Pradana Institut Teknologi Telkom Purwokerto
Sevia Indah Purnama Institut Teknologi Telkom Purwokerto
Shinta Romadhona Institut Teknologi Telkom Purwokerto

DOI:

https://doi.org/10.30595/jrre.v6i1.22090

Keywords:

Acute Lymphoblastik Leukemia, Convolutional Neural Network, EfficientNetB0, Hyperparameter

Abstract

Leukimia merupakan jenis kanker darah yang keganasannya dapat berkembang dengan cepat. Penundaan penanganan akan berakibat fatal dalam waktu beberapa bulan. Proses diagnosa dengan cepat dilakukan dengan cara memanfaatkan pemrosesan citra. Sebuah sistem dirancang agar mampu menglasifikasikan penyakit leukimia tipe Acute Lymphoblastic Leukemia (ALL) ke dalam 4 kelas, yaitu : Benign, Early, (Pre) Precursor, dan Pro (Progenitor) dengan memanfaatkan salah satu arsitetktur dari Convolutional Neural Network (CNN) yaitu EfficienNet-B0. Skenario pengujian dilakukan terhadap hyperparameter pada arsitektur EfficienNet-B0 melalui epoch (20, 30 dan 50) dengan learning rate (0.0001, 0.0003, 0.001, 0.003) dan optimizer jenis Adam dan RMSProp. Hasilnya adalah nilai performa akurasi pada data train mencapai 97,84% dan pada data test sebesar 98, 48%.

References

[1] A. Rehman, N. Abbas, T. Saba, S. I. ur Rahman, Z. Mehmood, and H. Kolivand, “Classification of acute lymphoblastic leukemia using deep learning,” Microsc Res Tech, vol. 81, no. 11, pp. 1310–1317, 2018, doi: 10.1002/jemt.23139.

[2] M. Z. Ullah et al., “An attention-based convolutional neural network for acute lymphoblastic leukemia classification,” Applied Sciences (Switzerland), vol. 11, no. 22, 2021, doi: 10.3390/app112210662.

[3] A. R. Praida, “Pengenalan Penyakit Darah menggunakan Teknik Pengolahan Citra dan Jaringan Syaraf Tiruan,” Thesis, pp. 1–9, 2008.

[4] J. E. Cortes, H. Kantarjian, and E. J. Freireich, “Acute lymphocytic leukemia: a comprehensive review with emphasis on biology and therapy.,” Cancer Treat Res, vol. 84, pp. 291–323, 1996, doi: 10.1007/978-1-4613-1261-1_13.

[5] T. T. P. Thanh, C. Vununu, S. Atoev, S.-H. Lee, and K.-R. Kwon, “Leukemia Blood Cell Image Classification Using Convolutional Neural Network,” International Journal of Computer Theory and Engineering, vol. 10, no. 2, pp. 54–58, 2018, doi: 10.7763/ijcte.2018.v10.1198.

[6] D. Kumar et al., “Automatic Detection of White Blood Cancer from Bone Marrow Microscopic Images Using Convolutional Neural Networks,” IEEE Access, vol. 8, no. Mm, pp. 142521–142531, 2020, doi: 10.1109/ACCESS.2020.3012292.

[7] W. Yu et al., “Automatic classification of leukocytes using deep neural network,” Proceedings of International Conference on ASIC, vol. 2017-Octob, pp. 1041–1044, 2017, doi: 10.1109/ASICON.2017.8252657.

[8] M. J. Macawile, V. V. Quiñones, A. B. Jr, J. Dela Cruz, and M. V. Caya, “2018 3rd International Conference on Control and Robotics Engineering : White Blood Cell Classification and Counting Using Convolutional Neural Network,” Nagoya Kōgyō Daigaku Institute of Electrical and Electronics Engineers, pp. 259–263, 2018.

[9] N. Sampathila et al., “Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images,” Healthcare (Switzerland), vol. 10, no. 10, 2022, doi: 10.3390/healthcare10101812.

[10] A. R. Revanda, C. Fatichah, and N. Suciati, “Classification of Acute Lymphoblastic Leukemia on White Blood Cell Microscopy Images Based on Instance Segmentation Using Mask R-CNN,” International Journal of Intelligent Engineering and Systems, vol. 15, no. 5, pp. 625–637, 2022, doi: 10.22266/ijies2022.1031.54.

[11] S. Shafique and S. Tehsin, “Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks,” Technol Cancer Res Treat, vol. 17, pp. 1–7, 2018, doi: 10.1177/1533033818802789.

[12] J. Prellberg and O. Kramer, “Acute lymphoblastic leukemia classification from microscopic images using convolutional neural networks,” Lecture Notes in Bioengineering, pp. 53–61, 2019, doi: 10.1007/978-981-15-0798-4_6.

[13] M. Claro et al., “Convolution Neural Network Models for Acute Leukemia Diagnosis,” International Conference on Systems, Signals, and Image Processing, vol. 2020-July, pp. 63–68, 2020, doi: 10.1109/IWSSIP48289.2020.9145406.

[14] S. Perveen, A. Alourani, M. Shahbaz, U. Ashraf, and I. Hamid, “A framework for Early Detection of Acute Lymphoblastic Leukemia and its Subtypes from Peripheral Blood Smear Images Using Deep Ensemble Learning Technique,” IEEE Access, vol. 12, no. February, pp. 29252–29268, 2024, doi: 10.1109/ACCESS.2024.3368031.

[15] T. Terwilliger and M. Abdul-Hay, “Acute lymphoblastic leukemia: a comprehensive review and 2017 update,” Blood Cancer J, vol. 7, no. 6, 2017, doi: 10.1038/BCJ.2017.53.

[16] A. Howard et al., “Searching for mobileNetV3,” Proceedings of the IEEE International Conference on Computer Vision, vol. 2019-Octob, pp. 1314–1324, 2019, doi: 10.1109/ICCV.2019.00140.

[17] cktavia N. Putri, “Implementasi Metode CNN Dalam Klasifikasi Gambar Jamur Pada Analisis Image Processing (Studi Kasus: Gambar Jamur Dengan Genus Agaricus Dan Amanita),” pp. 1–80, 2020.

[18] M. S. Wibawa, “Pengaruh Fungsi Aktivasi, Optimisasi dan Jumlah Epoch Terhadap Performa Jaringan Saraf Tiruan,” Jurnal Sistem dan Informatika (JSI), vol. 11, no. December, pp. 167–174, 2017, doi: 10.13140/RG.2.2.21139.94241.

[19] I. R. W. Putra, “Deteksi Jenis Buah-Buahan Menggunakan Deep,” Fakultas Teknologi dan Informatika Universitas Dinamika, p. 60, 2020.

[20] A. Rohim, Y. A. Sari, and Tibyani, “Convolution neural network (cnn) untuk pengklasifikasian citra makanan tradisional,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 3, no. 7, pp. 7038–7042, 2019.

[21] H. Mubarok, “Identifikasi Ekspresi Wajah Berbasis Citra Menggunakan Algoritma Convolutional Neural Network (CNN),” Universitas Islam Negeri Maulana Malik Ibrahim Malang, vol. 3, no. 1, pp. 10–12, 2019.