Leukemia Classification using a Convolutional Neural Network of AML Images
DOI:
https://doi.org/10.11113/mjfas.v19n3.2901Keywords:
Leukemia, classification, CNN, AML, ALL-ADB1Abstract
Among the most pressing issues in the field of illness diagnostics is identifying and diagnosing leukemia at its earliest stages, which requires accurate distinction of malignant leukocytes at a low cost. Leukemia is quite common, yet laboratory diagnostic centres often lack the necessary technology to diagnose the disease properly, and the available procedures take a long time. They are considering the efficacy of machine learning (ML) in illness diagnostics and that deep learning as a machine learning method is becoming critical. This study proposes a convolutional neural network (CNN) deep learning model for leukemia diagnosis utilizing the AML (acute myeloid leukemia) dataset. The classification using the proposed method achieved results that exceeded 98% accuracy, the sensitivity of 94.73% and specificity of 98.87%.
References
Wu, Y., Deng, Y., Wei, B., Xiang, D., Hu, J., Zhao, P., ... & Dai, Z. (2022). Global, regional, and national childhood cancer burden, 1990–2019: An analysis based on the Global Burden of Disease Study 2019. Journal of Advanced Research, 40, 233-247.
Dhillon, P. K., Mathur, P., Nandakumar, A., Fitzmaurice, C., Kumar, G. A., Mehrotra, R., ... & Dandona, L. (2018). The burden of cancers and their variations across the states of India: The Global Burden of Disease Study 1990–2016. The Lancet Oncology, 19(10), 1289-1306.
Küpeli Akkol, E., Genç, Y., Karpuz, B., Sobarzo-Sánchez, E., & Capasso, R. (2020). Coumarins and coumarin-related compounds in pharmacotherapy of cancer. Cancers, 12(7), 1959.
Goroshchuk, O., Kolosenko, I., Vidarsdottir, L., Azimi, A., & Palm-Apergi, C. (2019). Polo-like kinases and acute leukemia. Oncogene, 38(1), 1-16.
Lins, M. M., Mello, M. J. G., Ribeiro, R. C., De Camargo, B., de Fátima Pessoa Militão de Albuquerque, M., & Thuler, L. C. S. (2019). Survival and risk factors for mortality in pediatric patients with acute myeloid leukemia in a single reference center in low–middle-income country. Annals of Hematology, 98, 1403-1411.
Shabeykin, A. A., Gulyukin, A. M., Stepanova, T. V., Kozyreva, N. G., & Ivanova, L. A. (2019, August). Risk assessment for interspecies transmission of enzootic bovine leukemia. IOP Conference Series: Earth and Environmental Science, 315(4), 042036). IOP Publishing.
Lampson, B. L., Tyekucheva, S., Crombie, J. L., Kim, A. I., Merryman, R. W., Lowney, J., ... & Davids, M. S. (2019). Preliminary safety and efficacy results from a phase 2 study of acalabrutinib, venetoclax and obinutuzumab in patients with previously untreated chronic lymphocytic leukemia (CLL). Blood, 134, 32.
Abas, S. M., Abdulazeez, A. M., & Zeebaree, D. Q. (2021). A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia. Indones. J. Electr. Eng. Comput. Sci, 25(1).
Rege, M. V., Abdulkareem, M. B., Gaikwad, S., & Gawli, B. W. (2018, April). Automatic leukemia identification system using otsu image segmentation and mser approach for microscopic smear image database. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT) (pp. 267-272). IEEE.
Shallis, R. M., Wang, R., Davidoff, A., Ma, X., & Zeidan, A. M. (2019). Epidemiology of acute myeloid leukemia: Recent progress and enduring challenges. Blood reviews, 36, 70-87.
Allodji, R. S., Schwartz, B., Veres, C., Haddy, N., Rubino, C., Le Deley, M. C., ... & Diallo, I. (2015). Risk of subsequent leukemia after a solid tumor in childhood: impact of bone marrow radiation therapy and chemotherapy. International Journal of Radiation Oncology* Biology* Physics, 93(3), 658-667.
Kadhim, K. A., Mohamed, F., & Khudhair, Z. N. (2021, April). Deep learning: Classification and automated detection earlier of Alzheimer’s disease using brain MRI images. Journal of Physics: Conference Series,. 1892(1), 012009. IOP Publishing.
Kadhim, K. A., Mohamed, F., Khudhair, Z. N., & Alkawaz, M. H. (2020, November). Classification and predictive diagnosis earlier Alzheimer’s disease using MRI brain images. 2020 IEEE Conference on Big Data and Analytics (ICBDA) (pp. 45-50). IEEE.
Najjar, F. H., Al-Jawahry, H. M., Al-Khaffaf, M. S., & Al-Hasani, A. T. (2021, April). A novel hybrid feature extraction method using LTP, TFCM, and GLCM. Journal of Physics: Conference Series, 1892(1), 012018. IOP Publishing.
Waheed, S. R., Suaib, N. M., Rahim, M. S. M., Adnan, M. M., & Salim, A. A. (2021, April). Deep Learning Algorithms-based Object Detection and Localization Revisited. Journal of Physics: Conference Series, 1892(1), 012001). IOP Publishing.
Ayachi, R., Afif, M., Said, Y., & Atri, M. (2020). Strided convolution instead of max pooling for memory efficiency of convolutional neural networks. Proceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol. 1 (pp. 234-243). Springer International Publishing.
Jha, K. K., & Dutta, H. S. (2019). Mutual information based hybrid model and deep learning for acute lymphocytic leukemia detection in single cell blood smear images. Computer Methods and Programs in Biomedicine, 179, 104987.
Chang, J., Zhang, L., Gu, N., Zhang, X., Ye, M., Yin, R., & Meng, Q. (2019). A mix-pooling CNN architecture with FCRF for brain tumor segmentation. Journal of Visual Communication and Image Representation, 58, 316-322.
Chen, D., Li, J., & Xu, K. (2020). Arelu: Attention-based rectified linear unit. arXiv preprint arXiv:2006.13858.
Pham, T. C., Tran, C. T., Luu, M. S. K., Mai, D. A., Doucet, A., & Luong, C. M. (2020, October). Improving binary skin cancer classification based on best model selection method combined with optimizing full connected layers of Deep CNN. In 2020 International Conference on Multimedia Analysis and Pattern Recognition (MAPR) (pp. 1-6). IEEE.
Metzgar, D., Osuna, M., Kajon, A. E., Hawksworth, A. W., Irvine, M., & Russell, K. L. (2007). Abrupt emergence of diverse species B adenoviruses at US military recruit training centers. The Journal of Infectious Diseases, 196(10), 1465-1473.
Stone, S. L. (2019). Role of the ubiquitin proteasome system in plant response to abiotic stress. International Review of Cella Molecular Biology, 343, 65-110.
Santra, G., & Martin, J. M. (2019, December). Some observations on the performance of the most recent exchange-correlation functionals for the large and chemically diverse GMTKN55 benchmark. AIP Conference Proceedings, 2186(1), 030004. AIP Publishing LLC.
Nomori, H., Watanabe, K., Ohtsuka, T., Naruke, T., Suemasu, K., & Uno, K. (2004). The size of metastatic foci and lymph nodes yielding false-negative and false-positive lymph node staging with positron emission tomography in patients with lung cancer. The Journal of Thoracic and Cardiovascular Surgery, 127(4), 1087-1092.
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Copyright (c) 2023 MUHAMMAD SAFWAN ABD AZIZ, Karrar A. Kadhim, Fallah H Najjar, Ali Abdulhussein Waad, Ibrahim H Al-Kharsan, Zaid Nidhal Khudhair, Ali Aqeel Salim
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
