Leukemia is a cancerous disease characterised by an uncontrollable development of abnormal White Blood Cells (WBC). The identification of acute leukaemia is based on the percentage of WBC in the peripheral blood. In practice, the manual microscopic examination methods are used for acute leukemia detection. Despite the use of hardware autofocus mechanisms, large image collections acquired by automated microscopes often contain some fraction of low quality, out-of-focus images. More complicated cell morphology, with a wide range of size, border, position, and colour contrast were also obtained. Moreover, when the images are captured, the contrast between the cell border and the background in peripheral blood smears is influenced by the lighting position, and the effects of unwanted noise on blood leukemia images can results .in inaccurate diagnosis. So, an efficient pre-processing method is required to highlights the edges of nuclei. This paper describes in detail about the proposed Image Pre-Processing Techniques with Deep Learning Method for Detecting Leukemia in Microscopic Blood Images. This automated system will detect leukemia cells from the blood cancer affected patient’s collected blood sample. The image processing techniques used for the diagnosis include optimized contrast stretching (OCS) to enhance the image and detect the nuclei, also the k-means clustering algorithm for nuclei segmentation. A features extraction based on geometry, colour, texture, and statistics information are extracted, as well as fuzzy rule based decision system are performed to get better results of leukemia detection.

Leukemia, microscopic examination, Deep Learning method, contrasts stretching, k-means clustering

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