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6724 Blood Cancer Images | Open Access Journals
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Accounting & Marketing

ISSN: 2168-9601

Open Access

6724 Blood Cancer Images

Image segmentation is the process, which is used to subdivide an image into its component regions or objects. Here, the objects are white blood cells. White blood cells are divided into five types. They are neutrophil, eosinophil, basophil, monocyte and lymphocyte. The main cause of blood cancer is the rapid increase of lymphocyte. So, lymphocytes are detected. Then, feature extraction method is applied. Image features are local, meaningful, detectable parts of an image. Here, the features of lymphocytes such as area, perimeter and circularity are calculated. Image classification is a computational procedure that sorts images into groups or classes according to their similarities. Here, the normal lymphocyte cells and the blast cells are classified with the help of these extracted features using kNN classifier. kNN(k-Nearest Neighbor classifier) is a simple supervised classification method. It stores the normal values of normal lymphocyte cell’s features. Then, it compares the values with that of the blast cells. The blood cancer cells are detected from a patient’s blood smear using different techniques of image process.

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