Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms

Acharya, Vasundhara and Kumar, Preetham (2019) Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms. Medical and Biological Engineering and Computing. pp. 1-29. ISSN 0140-0118

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Blood is composed of white blood cells, red blood cells, and platelets. Segmentation of the blood smear cells and extraction of featuresofthecellsisessentialinthefieldofmedicine.Acutelymphoblasticleukemiaisaformofbloodcancercausedduetothe abnormal increase in the production of immature white blood cells in the bone marrow. It mostly affects the children below 5yearsandadultsabove50yearsofage.Duetothelatediagnosisandcostofthedevicesusedforthedetermination,themortality rate has increased drastically. Flow cytometry technique that performs automated counting fails to identify the abnormal cells. Manual recount performed using hemocytometer are prone to errors and are imprecise. The proposed work aims to survey different computer-aided system techniques used to segment the blood smear image. The primary objective here is to derive knowledgefromthedifferentmethodologiesusedforextractingfeaturesfromwhitebloodcellsanddevelopasystemthatwould accuratelysegment thebloodsmear imagebyovercomingthedrawbacksofthepreviousworks.Theobjectivementionedabove is achieved in two ways. Firstly, a novel algorithm is developed to segment the nucleus and cytoplasm of white blood cell. Secondly, a model is built to extract the features and train the model. The different supervised classifiers are compared, and the one with the highest accuracy is used for the classification. Six hundred images are used in the experimentation. InfoGainAttributeEvalandtheRankerSearchmethodareusedtoachievethefeatureselectionwhichinturnhelpsinimprovising the classifier performance. The result shows the classification of the acute lymphoblastic leukemia into its three respective categories namely: ALL-L1, ALL-L2, ALL-L3. The model can differentiate between a normal peripheral blood smear and an abnormalbloodsmear.Theextractedfeaturevaluesofacancerouscellandanormalcellarealsoshown.Theperformanceofthe model is evaluated using the test images stained with various stains. The proposed algorithm achieved an overall accuracy of 98.6%. The promising results show that it can be used as a diagnostic tool by the pathologists

Item Type: Article
Uncontrolled Keywords: Acute lymphoblastic leukemia . Blood smear cells . Flow cytometry . Hemocytometer . White blood cell
Subjects: Engineering > MIT Manipal > Computer Science and Engineering
Engineering > MIT Manipal > Information and Communication Technology
Depositing User: MIT Library
Date Deposited: 06 Jul 2019 06:47
Last Modified: 06 Jul 2019 06:47

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