Automated and Enhanced Leucocyte Detection and Classification for Leukemia Detection Using Multi-Class SVM Classifier

Abstract

In this day and age, surrounded by innumerable forms of technology, the use of various autonomous systems to recognize various ailments has tremendously benefited the medical industry. An important medical practice is the visual evaluation and counting of white blood cells in microscopic peripheral blood smears. Invaluable details regarding the patient’s health may be revealed, such as the discovery of acute lymphatic leukaemia or other serious disorders. This study provides a paradigm for detecting acute lymphoblastic leukemia from a microscopic vision of white blood cells. Microscopic images must go through a thorough pre-processing phase before being classified. In this study, WBCs are separated from blood smear images using morphological techniques, and the segmented region is then searched for a set of textural, geometrical, and statistical properties. Four different machine learning techniques are used to examine the performance of these algorithms: random forest (RF), support vector machine (SVM), naive Bayes classifier (NB), and K nearest neighbor (KNN). The SVM is effective in classifying and identifying the acute lymphoblastic cell that produces leukemia malignancy, as can be observed after careful comparison. A single classifier is virtually completely useless given the variety of blood smear pictures. As a result, we considered using EMC-SVM to classify leukocytes. The suggested method successfully distinguishes white blood cells from sample blood smear images, and accurately categorizes each segmented cell into the relevant group.

Keywords

• lymphoblastic
• leukemia
• segmentation
• feature extraction
• PCA
• multiclass classifier