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creating automated processes in different areas of medical science with theapplication of engineering tools is a highly growing field over recent decades.In this context, many medical image processing and analyzing researchers useworthwhile methods in artificial intelligence, which can reduce necessary humanpower while increases accuracy of results. Among various medical images, bloodmicroscopic images play a vital role in heart failure diagnosis, e.g., bloodcancers. The prominent component in blood cancer diagnosis is white blood cells(WBCs) which due to its general characteristics in microscopic images sometimesmake difficulties in recognition and classification tasks such as non-uniformcolors/illuminances, different shapes, sizes, and textures. Moreover,overlapped WBCs in bone marrow images and neighboring to red blood cells areidentified as reasons for errors in the classification task. In this paper, wehave endeavored to segment various parts in medical images via Na\"ive Bayesclustering method and in next stage via TSLDA classifier, which is supplied byfeatures acquired from covariance descriptor results in the accuracy of 98.02%.It seems that this result is delightful in WBCs recognition.

High Accuracy Classification of White Blood Cells using TSLDA Classifier and Covariance Features