Cancer is a deadly disease. Initial detection of cancer is the best way to cure the disease. Medical Image Processing plays an essential part in the detection of disease. Leukemia is a kind of blood cancer that happens due to irregular or immature White Blood Cell (WBC) s. In general, WBC is the fighter to fight against infectious cells in the human body. Abnormal growth of WBC from bone marrow will destroy the other cells and affect bone marrow and lymphatic tissues. These cells do not function properly and leads to leukemia. In olden days, identification of disease and cell counting in the blood were very complicated. In the medical sector, they use a device called Haemocytometer which counts the amount of cells in the blood manually. But it takes more time for counting and gives inaccurate results. To overcome these issues, a software based solution is given with the help of microscopic images. With this image processing technique, the number of RBCs, WBCs and platelets are calculated and also whether a person can be affected by leukemia or not is identified.


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    Titel :

    Blood Cancer Detection using Machine Learning


    Beteiligte:
    Saranyan, N (Autor:in) / Kanthimathi, N (Autor:in) / Ramya, P (Autor:in) / Kowsalya, N (Autor:in) / Mohanapriya, S (Autor:in)


    Erscheinungsdatum :

    2021-12-02


    Format / Umfang :

    838298 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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