Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10127207 | Biomedical Signal Processing and Control | 2019 | 9 Pages |
Abstract
This paper presents an effective scheme for classification of the normal white blood cells from the affected cells in a microscopic image. The proposed method initially pre-processes the input images using Y component of the CMYK image and a triangle method of thresholding. Subsequently, it utilizes discrete orthonormal S-transform (DOST) to extract the texture features, and its dimensionality is reduced using linear discriminant analysis. The reduced features are then supplied to the proposed Adaboost algorithm with RF (ADBRF) classifier where the random forest is used as the base classifier. A publicly available dataset, ALL-IDB1 is used to validate the proposed scheme. The simulation results based on the five runs of k-fold stratified cross-validation indicate that the proposed method yields superior accuracy (99.66%) as compared to existing schemes.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Sonali Mishra, Banshidhar Majhi, Pankaj Kumar Sa,