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Leukemia is a blood cancer which results due to inundation of abnormal white blood cells in human. Digital pathology is a dynamic condition that empowers extraction of precise and detailed information of amount and condition of WBCs from patient’s blood smear. Digital pathology methods along with machine learning is gaining importance speedily as a proven and essential technology for investigation of new features of leukemia patients histological information, providing the reduced laboratory expenses, improved operational efficiency, diagnosis. This paper highlights emerging methods to automatically diagnose leukemia from cytological-histological & morphological analyses. In this study, we have performed comparative analysis of white blood cells segmentation techniques and evaluated the performance of pretrained deep CNN with multiclass models for Support Vector Machine (ECOC) as feature extractors towards classification of WBC to assist in improved screening of leukemia with classification accuracy of 93.94%.
Keywords : Blood Cancer, Microscopic Images, White Blood Cells, Blood Smear, Acute Leukemia, Machine Learning, Classification, Convolutional Neural Network
Blood cancer influences the creation and capacity of platelets in human. Leukemia is one of the types of blood cancer that begins from bone marrow, where blood is produced . Stem cells mature and develop into three types of blood cells: redblood cells, white blood cells, or platelets. In blood cancer, the healthy blood cell development process is hindered by uncontrolled progress of an unusual type of blood cell. These abnormal blood cells, prevent blood from fighting off infections or preventing serious bleeding. The visual investigation of peripheral blood samples is a vital test in the methods for the identification of leukemia . There are four categories of leukemia
In this work we have proposed an innovative method for a classification of White blood cells from microscopic images, as support for detection of leukemia. The results obtained shows that the proposed method is able to classify the white blood cells present in the blood smear image with the accuracy of 93.94% by modifying a pretrained deep convolutional neural network
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