Abstract
The earlier method for diagnosing or detecting some diseases was through Manual Examination of Blood Smear Image and had a lot of human errors and tedious work. Overcome these issues modern methods like segmentation with efficient data mining techniques and classify leukocytes (WBC) based on machine learning algorithms are implemented. With the aid of the image processing technique, automatically diagnoses the diseases using the features of WBCs. For accurate diagnosing of disease, correctly classified leukocytes, and its subclass are required. Geometrical, textural, and statistical features of different images are extracted and applied in classification algorithms. Hence, multi level-based classification developed using the different classifiers like LibSVM, Naive Bayes, J48, Zero R, PART and Random Forest classifiers are considered and select the best classifier which is used to efficiently classify each category. From the performance indices, the best technique can suit for the identification of disease like Leukaemia. The proposed work in this paper demonstrated using MATLAB in GUI Environment. Classification is developed using WEKA software. The segmentation of different testing and training images have done Using 5 fold Cross Validation techniques performance indices measured and classification accuracy was compared in this paper.
Published Version
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