A New Approach for Leukemia Identification based on Cepstral Analysis and Wavelet Transform
Amira Samy, Faisal Amir · International Journal of Advanced Computer Science and Applications · 2017
This paper implements a new leukemia identification method which depends on Mel frequency cepstral coefficient (MFCC) feature extraction and wavelet transform. Leukemia identification is a measurement of blood cell features for detecting the blood cancer of a patient. Blood cell feature extraction is based on transforming the blood cell two dimensional (2D) image into one dimensional (1D) signal and thereafter extracting MFCCs from such signal. Furthermore, discrete wavelet transform (DWT) of the 1D blood cell signals are used for extracting extra MFCCs features to assist the identification procedure. In addition, Wavelet transform with denoising is used to reduce noise and increase classification accuracy. Feature matching/classification of the blood cell to be a normal cell or leukemia cell is performed in the proposed method using five different classifiers. Experimental results of leukemia identification method show that the proposed method is very good with wavelet transform and robust in the presence of noise.