T. Dharani and S. Hariprasath, “Diagnosis of leukemia and its types using digital image processing techniques,” in 2018 3rd International Conference on Communication and Electronics Systems (ICCES), 2018, pp. 275–279.
S. Shafique and S. Tehsin, “Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks,” vol. 17, pp. 1–7, 2018, doi: 10.1177/1533033818802789.
P. Pandey, “Cancer of White Blood Cells: Blood Cancer,” J Tum Res Rep, vol. 6, p. 141, 2021.
WHO, “World Health Organization,” 2018. .
M. Ghaderzadeh, F. Asadi, A. Hosseini, D. Bashash, H. Abolghasemi, and A. Roshanpour, “Machine learning indetection and classification of leukemia using smear blood images: a systematic review,” Sci. Program., vol. 2021, 2021.
M. E. Billah and F. Javed, “Bayesian Convolutional Neural Network-based Models for Diagnosis of Blood Cancer,” Appl. Artif. Intell., vol. 36, no. 1, p. 2011688, 2022.
R. Sigit, M. M. Bachtiar, and M. I. Fikri, “Identification of Leukemia diseases based on microscopic human blood cells using image processing,” in 2018 International Conference on Applied Engineering (ICAE), 2018, pp. 1–5.
J. Rangole, “Detection of Leukemia in Microscopic Images Using Image Processing,” in International Conference on Communication and Signal Processing, 2019, no. April 2014, doi: 10.1109/ICCSP.2014.6949840.
J. Rawat, A. Singh, H. S. Bhadauria, and J. Virmani, “Computer Aided Diagnostic System for Detection of Leukemia,” Int. Conf. Eco-friendly Comput. Commun. Syst., vol. 70, pp. 748–756, 2015, doi: 10.1016/j.procs.2015.10.113.
A. Harshavardhan, S. Babu, and T. Venugopal, “Analysis of feature extraction methods for the classification of brain tumor detection,” Int. J. Pure Appl. Math., vol. 117, no. 7, pp. 147–155, 2017.
D. C. R. e Novitasari and T. Al., “Application of Feature Extraction for Breast Cancer using One Order Statistic, GLCM, GLRLM, and GLDM,” Adv. Sci. Technol. Eng. Syst. J., vol. 4, no. 4, pp. 114–120, 2019.
A. Z. Foeady, D. C. R. Novitasari, A. H. Asyhar, and M. Firmansjah, “Automated Diagnosis System of Diabetic Retinopathy Using GLCM Method and SVM Classifier,” Proceeding Electr. Eng. Comput. Sci. Informatics, vol. 5, no. 1, pp. 154–160, 2018.
A. N. Alfiyatin, W. F. Mahmudy, C. F. Ananda, and Y. P. Anggodo, “Penerapan Extreme Learning Machine (ELM) untuk Peramalan Laju Inflasi di Indonesia,” J. Teknol. Inf. dan Ilmu Komput., vol. 6, no. 2, p. 179, 2019, doi: 10.25126/jtiik.201962900.
S. Suhaeri, N. M. Nawi, and M. Fathurahman, “Early Detection of Dengue Disease Using Extreme Learning Machine,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 8, no. 5, pp. 2219–2224, 2018.
M. Nemissi, H. Salah, and H. Seridi, “Breast cancer diagnosis using an enhanced Extreme Learning Machine based-Neural Network,” in International Conference on Signal, Image, Vision and their Applications (SIVA), 2018, pp. 1–4, doi: 10.1109/SIVA.2018.8661149.
R. Gowthaman, “Automatic Identification and Classification of Microaneurysms for Detection of Diabetic Retinopathy,” Int. J. Res. Eng. Technol., vol. 3, no. 02, pp. 464–473, 2014.
R. D. Labati, V. Piuri, and F. Scotti, “All-IDB: The acute lymphoblastic leukemia image database for image processing,” in 2011 18th IEEE international conference on image processing, 2011, pp. 2045–2048.
F. D. Aferi, T. W. Purboyo, and R. E. Saputra,“Cotton texture segmentation based on image texture analysis using gray level co-occurrence matrix (GLCM) and Euclidean distance,” Int. J. Appl. Eng. Res, vol. 13, no. 1, pp. 449–455, 2018.
B. Azam et al., “A reliable auto-robust analysis of blood smear images for classification of microcytic hypochromic anemia using gray level matrices and gabor feature bank,” Entropy, vol. 22, no. 9, p. 1040, 2020.
S. Punitha, A. Amuthan, and K. S. Joseph, “Benign and malignant breast cancer segmentation using optimized region growing technique,” Futur. Comput. Informatics J., vol. 3, no. 2, pp. 348–358, 2018, doi: 10.1016/j.fcij.2018.10.005.
K. Preetha and S. K. Jayanthi, “GLCM and GLRLM based Feature Extraction Technique in Mammogram Images,” Int. J. Eng.Technol. Website, vol. 7, pp. 266–270, 2018.
S. S. Xu, C. Chang, C. Su, and P. Q. Phu, “Classification of Liver Diseases Based on Ultrasound Image Texture Features,” J. Appl. Sci. MDPI, vol. 9, pp. 1–25, 2019, doi: 10.3390/app9020342.
Q. Humaini, “Jaringan Syaraf Tiruan Extreme Learning Machine (ELM) untuk Memprediksi Kondisi Cuaca di Wilayah Malang,” Univ. Islam Negeri Maulana Malik Ibrahim Malang, 2015.
B. Y. Phiadelvira, D. Z. Haq, and D. C. R. Novitasari, “Prediksi Besar Daya Listrik Tenaga Gelombang Laut Metode Oscillating Water Coloumn (PLTGL-OWC) Di Perairan Banyuwangi Menggunakan Extreme Learning Machine (Elm),” UNNES J. Math., 2021.
I. Ahmad, M. Basheri, M. J. Iqbal, and A. Rahim, “Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection,” IEEE access, vol. 6, pp. 33789–33795, 2018.
S. Ding, H. Zhao, Y. Zhang, X. Xu, and R. Nie, “Extreme learning machine: algorithm, theory and applications,” Artif. Intell. Rev., vol. 44, no. 1, pp. 103–115, 2015, doi: 10.1007/s10462-013-9405-z.
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© Computer Engineering and Applications Journal, 2022
Wahyu Tri Puspitasari
UIN Sunan Ampel Surabaya
Dina Zatusiva Haq
UIN Sunan Ampel Surabaya
Dian C Rini Novitasari
UIN Sunan Ampel Surabaya
Leukaemia Identification based on Texture Analysis of Microscopic Peripheral Blood Images using Feed-Forward Neural Network
Vol 11 No 3 (2022)
Submitted: Apr 27, 2022
Published: Oct 1, 2022
Leukaemia is very dangerous because it includes liquid tumour that it cannot be seen physically and is difficult to detect. Alternative detection of Leukaemia using microscopy can be processed using a computing system. Leukemia disease can be detected by microscopic examination. Microscopic test results can be processed using machine learning for classification systems. The classification system can be obtained using Feed-Forward Neural Network. Extreme Learning Machine (ELM) is a neural network that has a feedforward structure with a single hidden layer. ELM chooses the input weight and hidden neuron bias at random to minimize training time based on the Moore Penrose Pseudoinverse theory. The classification of Leukaemia is based on microscopic peripheral blood images using ELM. The classification stages consist of pre-processing, feature extraction using GLRLM, and classification using ELM. This system is used to classify Leukaemia into three classes, that is acute lymphoblastic Leukaemia, chronic lymphoblastic Leukaemia, and not Leukaemia. The best results were obtained in ten hidden nodes with an accuracy of 100%, a precision of 100%, a withdrawal of 100%.