Abstract
In current times, pathologists visually inspect blood cell images under the microscope for the purpose of identifying blood disorders. Identified blood disorders are classified into several blood diseases. Our work aims at studying and designing a model (framework) for the detection of leukemia (blood cancer) and its types using microscopic blood sample images and analyzing them for diagnosis at an earlier stage. Earlier, hematologists used to visually inspect the microscopic images, thereby making the diagnosis process error-prone and time-consuming. However, the use of newly developed automatic image processing systems manages to successfully overcome most of the visual inspection related drawbacks. The early diagnosis of blood cancer will greatly aid in better treatment. In this process, the acquired dataset images are taken as inputs and the images are sent through different image processing techniques such as image enhancement (preprocessing), segmentation, feature extraction and classification. The method proposed is applied to a large number of images of varying quality and is found to provide satisfactory results.
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Dutta, M., Karmakar, S., Banerjee, P., Ghatak, R. (2020). Detection of Leukemia in Blood Samples Applying Image Processing Using a Novel Edge Detection Method. In: Mandal, J., Mukhopadhyay, S. (eds) Proceedings of the Global AI Congress 2019. Advances in Intelligent Systems and Computing, vol 1112. Springer, Singapore. https://doi.org/10.1007/978-981-15-2188-1_1
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DOI: https://doi.org/10.1007/978-981-15-2188-1_1
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