High Accuracy Classification of White Blood Cells using TSLDA Classifier and Covariance Features
This work addresses automated WBC classification for medical diagnosis, such as blood cancer detection, but appears incremental as it combines existing methods like Naïve Bayes clustering and TSLDA.
The paper tackled the problem of classifying white blood cells (WBCs) in medical images, which is challenging due to variations in color, shape, and overlapping cells, and achieved an accuracy of 98.02% using a TSLDA classifier with covariance features.
creating automated processes in different areas of medical science with the 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 play a vital role in heart failure diagnosis, e.g., blood cancers. The 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 the classification task. 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 in the accuracy of 98.02%. It seems that this result is delightful in WBCs recognition.