Detection and Classification of Leukemia using MPFCM Segmentation and Random Forest with Boosting Techniques

Abstract

Identification of blood disorders is through visual inspection of microscopic blood cell images. From the identification of blood disorders lead to classification of certain diseases related to blood. We propose an automatic segmentation method for segmenting White blood cell images. Firstly, modified possibilistic fuzzy c-means algorithm is proposed to detect the contours in the image. The GLCM features are extracted and features are selected by MRMR. Adaptive boosting and LS Boosting has been utilized to classify blast cells from normal lymphocyte cells. Comparison performance of classification accuracy was carried out. The effectiveness of the classification system is tested with the total of 80 samples collected. The evaluated results demonstrate that our method outperformed the existing systems with an accuracy of 88  %.