Abstract
Leukemia which is commonly known as blood cancer is a fatal type of cancer that affects white blood cells. It usually originates from the bone marrow and causes the development of abnormal blood cells called blasts. The diagnosis is made by blood tests and bone marrow biopsy which involve manual work and are time consuming. There is a need for development of an automatic tool for the detection of white blood cell cancer. Therefore, in this work, a classification model using Convolutional Neural Network with Deep Learning techniques as a basis is proposed. This work was implemented using Keras library with TensorFlow as backend. This model was trained and evaluated on cancer cell dataset C_NMC_2019 which includes white blood cell regions segmented from the microscopic blood smear images. The model offers an accuracy of 91% for training and 87% for testing which is satisfactory.
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Anagha, V., Disha, A., Aishwarya, B.Y., Nikkita, R., Biradar, V.G. (2022). Detection of Leukemia Using Convolutional Neural Network. In: Shetty, N.R., Patnaik, L.M., Nagaraj, H.C., Hamsavath, P.N., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Lecture Notes in Electrical Engineering, vol 789. Springer, Singapore. https://doi.org/10.1007/978-981-16-1338-8_20
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DOI: https://doi.org/10.1007/978-981-16-1338-8_20
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