On this page
Research Article | Open Access
Volume 14 2022 | None
ENHANCING ACCURACY IN DETECTION AND CLASSIFICATION OF WHITE BLOOD CANCER CELLS USING K-MEANS OVER MORPHOLOGICAL SEGMENTATION
BurlaGopi raju , S. Narendran
Pages: 5764-5773
Abstract
Aim: The main objective of the work is to improve the accuracy and specificity for automatic detection, classification, and counting of the number of cells using a Morphological segmentation algorithm compared with the K-Means clustering algorithm. Materials and Methods: Data set used for this research consists of 28 cancer images for the detection of different types of White blood cell (WBC) cancer diseases. Determination of sample size of the two groups is calculated using G power (power of 0.80 and alpha value of 0.05) and two groups are categorized as Morphological segmentation classifier (Group 1) and k-means clustering classifier (Group 2). For performance analysis, 70% of the images are used for training and 30% of images are used for testing and validation
Keywords
Innovative White Blood Cancer Detection, Segmentation, Morphological Segmentation Algorithm, K-Means Clustering, Cancer Disease, White Blood Cells.
PDF
56
Views
25
Downloads