Abstract
Acute Lymphoblastic Leukemia is an anomaly that affects White Blood Cells. This type of cancer occurs when there is an error in blood cell DNA. Children are prone to this type of cancer. After the initial stages, it spreads to other organs like the liver and spleen. The problem with this type of cancer is that, unlike other forms of cancer, it doesn’t cause any tumors hence it is very hard to detect. Manual testing methods were used before the automation, but it was time-consuming and very much prone to errors. To solve that problem, automated testing methods were introduced. Different systems [1, 3, 4] were introduced in the past, but most of them have variable accuracies. These Automated Systems [1, 3, 4] used image processing and unsupervised machine learning techniques to classify the images into cancerous and healthy. The proposed system uses Hybrid Hierarchical Classifiers to classify the cancer cells, which will be an improvement over the previous systems and solves the problem of variable accuracies.
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Sunil, S., Sonu, P., Sarath, S., Rahul Nath, R., Viswan, V. (2021). An Effective Approach for Classifying Acute Lymphoblastic Leukemia Using Hybrid Hierarchical Classifiers. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_14
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DOI: https://doi.org/10.1007/978-3-030-81462-5_14
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