Classification of Acute Lymphoblastic Leukemia based on White Blood Cell Images using InceptionV3 Model
Abstract
Acute lymphoblastic leukemia (ALL) is the most common form of leukemia that occurs in children. Detection of ALL through white blood cell image analysis can help with the prognosis and appropriate treatment. In this study, the author proposes an approach to classifying ALL based on white blood cell images using a convolutional neural network (CNN) model called InceptionV3. The dataset used in this research consists of white blood cell images collected from patients with ALL and healthy individuals. These images were obtained from The Cancer Imaging Archive (TCIA), which is a service that stores large-scale cancer medical images available to the public. During the evaluation phase, the author used training data evaluation metrics such as accuracy and loss to measure the model's performance. The research results show that the InceptionV3 model is capable of classifying white blood cell images with a high level of accuracy. This model achieves an average ALL recognition accuracy of 0.9896 with a loss of 0.031. The use of CNN models such as InceptionV3 in medical image analysis has the potential to improve the efficiency and precision of image-based disease diagnosis.
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K. D. Nugroho, “Studi Fenomenologi : Pengabaian Gejala Awal Kanker mengakibatkan Respon Negatif bagi Klien dan Keluarga,” Jurnal Keperawatan Malang, vol. 5, no. 1, pp. 46–54, Jun. 2020, doi: 10.36916/jkm.v5i1.105.
E. Ward, C. Desantis, ; Anthony Robbins, B. Kohler, and A. Jemal, “Childhood and adolescent cancer statistics, 2014,” CA Cancer J Clin, vol. 64, no. 2, pp. 83–103, Mar. 2014, doi: 10.3322/CAAC.21219.
V. S. Pojoh, M. F. J. Mantik, and J. I. Ch. Manoppo, “Hubungan Indeks Massa Tubuh dan Tercapainya Remisi pada Anak Penderita Leukemia Limfoblastik Akut,” e-CliniC, vol. 8, no. 1, Dec. 2019, doi: 10.35790/ecl.8.1.2020.27186.
Surveillance Epidemiology and End Result, “Leukemia — Cancer Stat Facts,” 2022. https://seer.cancer.gov/statfacts/html/leuks.html (accessed Oct. 08, 2022).
D. Gunawan and H. Setiawan, “Convolutional Neural Network dalam Analisis Citra Medis,” 2022.
A. Sarkar, T. Das, G. Das, and Z. Ghosh, “MicroRNA mediated gene regulatory circuits leads to machine learning based preliminary detection of acute myeloid leukemia,” Comput Biol Chem, vol. 104, p. 107859, Jun. 2023, doi: 10.1016/j.compbiolchem.2023.107859.
E. M. Nagiub Abdelsalam, K. F. Hussain, N. M. Omar, and Q. T. Ali, “Computer Aided Leukemia Detection using Microscopic Blood Image Based Machine Learning ‘Convolutional Neural Network,’” Clin Lymphoma Myeloma Leuk, vol. 18, p. S297, Sep. 2018, doi: 10.1016/j.clml.2018.07.246.
E. Nagiub, K. F. Hussain, N. Omar, and Q. Taher, “ALL-018 Application of Machine Learning in the Diagnosis of Acute Lymphoblastic Leukemia,” Clin Lymphoma Myeloma Leuk, vol. 22, p. S190, Oct. 2022, doi: 10.1016/S2152-2650(22)01174-0.
A. Abhishek, R. K. Jha, R. Sinha, and K. Jha, “Automated detection and classification of leukemia on a subject-independent test dataset using deep transfer learning supported by Grad-CAM visualization,” Biomed Signal Process Control, vol. 83, p. 104722, May 2023, doi: 10.1016/j.bspc.2023.104722.
K. K. Anilkumar, V. J. Manoj, and T. M. Sagi, “Automated detection of leukemia by pretrained deep neural networks and transfer learning: A comparison,” Med Eng Phys, vol. 98, pp. 8–19, Dec. 2021, doi: 10.1016/j.medengphy.2021.10.006.
H. Xu, J. Jia, H.-H. Jeong, and Z. Zhao, “Deep learning for detecting and elucidating human T-cell leukemia virus type 1 integration in the human genome,” Patterns, vol. 4, no. 2, p. 100674, Feb. 2023, doi: 10.1016/j.patter.2022.100674.
S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” JUSTINDO (Jurnal Sistem & Teknologi Informasi Indonesia), 2018.
M. Zakir Ullah et al., “An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification,” Applied Sciences, vol. 11, no. 22, p. 10662, Nov. 2021, doi: 10.3390/app112210662.
K. K. Anilkumar, V. J. Manoj, and T. M. Sagi, “Automated Detection of B Cell and T Cell Acute Lymphoblastic Leukaemia Using Deep Learning,” IRBM, vol. 43, no. 5, pp. 405–413, Oct. 2022, doi: 10.1016/J.IRBM.2021.05.005.
K. Joshi, V. Tripathi, C. Bose, and C. Bhardwaj, “Robust Sports Image Classification Using InceptionV3 and Neural Networks,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 2374–2381. doi: 10.1016/j.procs.2020.03.290.
T. Shawly and A. A. Alsheikhy, “Biomedical Diagnosis of Leukemia Using a Deep Learner Classifier,” Comput Intell Neurosci, vol. 2022, 2022, doi: 10.1155/2022/1568375.
M. Akter Hossain, M. Islam Sabik, I. Muntasir, A. K. M. Muzahidul Islam, S. Islam, and A. Ahmed, “Leukemia Detection Mechanism through Microscopic Image and ML Techniques,” in 2020 IEEE REGION 10 CONFERENCE (TENCON), IEEE, Nov. 2020, pp. 61–66. doi: 10.1109/TENCON50793.2020.9293925.
K. Clark et al., “The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository,” J Digit Imaging, vol. 26, no. 6, pp. 1045–1057, Dec. 2013, doi: 10.1007/s10278-013-9622-7.
A. Gupta and R. Gupta, “ALL Challenge dataset of ISBI 2019 [Data set],” The Cancer Imaging Archive, 2019, doi: https://doi.org/10.7937/tcia.2019.dc64i46r.
P. Kim, MATLAB Deep Learning. Berkeley, CA: Apress, 2017. doi: 10.1007/978-1-4842-2845-6.
N. R. Fauziyya, “Metoda Convolutional Neural Network (CNN) untuk Pendeteksi Tangga pada Alat Pemandu Arah bagi Penyandang Tunanetra,” Telekontran : Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan, vol. 8, no. 2, pp. 145–153, Apr. 2021, doi: 10.34010/telekontran.v8i2.4709.
S. Indolia, A. K. Goswami, S. P. Mishra, and P. Asopa, “Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach,” Procedia Comput Sci, vol. 132, pp. 679–688, 2018, doi: 10.1016/j.procs.2018.05.069.
N. K. C. PRATIWI, N. IBRAHIM, Y. N. FU’ADAH, and S. RIZAL, “Deteksi Parasit Plasmodium pada Citra Mikroskopis Hapusan Darah dengan Metode Deep Learning,” ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 9, no. 2, p. 306, Apr. 2021, doi: 10.26760/elkomika.v9i2.306.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, Jun. 2016, pp. 2818–2826. doi: 10.1109/CVPR.2016.308.
M. Ahmed, N. Afreen, M. Ahmed, M. Sameer, and J. Ahamed, “An inception V3 approach for malware classification using machine learning and transfer learning,” International Journal of Intelligent Networks, vol. 4, pp. 11–18, 2023, doi: 10.1016/j.ijin.2022.11.005.
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