White Blood Cells to Classify Leukemic Blood Images Using Deep Learning and Image Processing

White Blood Cell (WBC) count is a significant task in identifying leukemia, a widely known malignancy that can be devastating gradually. Infantile WBCs existing in the sponge tissues of bone marrow affect the superfluous expansion, which in turn produces leukemia cancer. Deep learning and image processing techniques models can be applied in the field to detect leukemic blood and generate outstanding outcomes. Leukemia occurs from the leukocyte blood type, which is one kind of white blood cell. This proposed system introduces a method of classifying leukemic blood images and counting the number of white blood cells in a Leukemic blood image. This is a hybrid procedure combining deep learning and image processing techniques. A collection of 221 blood cell images available on a website known as ‘RaabinData’ is used, which were collected from patients at the Takht-e Tavoos Medical School Laboratory in Tehran. Using this dataset, this study achieves 96.74% accuracy in counting white blood cells.