High Accuracy Classification of White Blood Cells using TSLDA Classifier and Covariance Features
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Creating automated processes in different areas of medical science with application of engineering tools is a highly growing field over recent decades. In this context, many medical image processing and analyzing researchers use worthwhile methods in artificial intelligence which can reduce necessary human power while increases accuracy of results. Among various medical images, blood microscopic images plays vital role in heart failure diagnosis e.g. blood cancers. Prominent component in blood cancer diagnosis is white blood cells (WBCs) which due to its general characteristics in microscopic images sometimes make difficulties in recognition and classification tasks such as non-uniform colors/illuminances, different shapes, sizes and textures. Moreover, overlapped WBCs in bone marrow images and neighboring to red blood cells are identified as reasons for errors in classification section. In this paper, we have endeavored to segment various parts in medical images via Naïve Bayes clustering method and in next stage via TSLDA classifier, which is supplied by features acquired from covariance descriptor results 98.02 is delightful in WBCs recognition.
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