Abstract
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.
Highlights
The probability of recovery of acute lymphocytic leukemia patient can be increased by the early identification of its symptoms
Mel frequency cepstral coefficient (MFCC) are applied in speech recognition methods and its values are not very powerful in existence of additive noise [6], and so we propose leukemia identification system as an application of this idea by transforming the leukemia image 2D object into 1D signals and executing the same processes performed on speech signals
This paper has given a strong identification method for leukemia identification images based on MFCC and the wavelet transform techniques
Summary
The probability of recovery of acute lymphocytic leukemia patient can be increased by the early identification of its symptoms. There is a lot of work for leukemia recognition based on many approaches like gene expression analysis [1] and holographic microscope images [2]. Artificial intelligent methods are based on automated systems that can speed up identification and make it much easier, in addition, the amount of data analyzed are higher increase the classification accuracy specially in telemedicine applications. Many prediction methods used for analysis and classification of leukemia like KNN algorithm [3], other prediction methods use endoscopic images technique [4] and image processing techniques [5]. This paper presents a fully automatic method for leukemia identification that classifies blood images to know if the blood cell is normal or leukemia (cancer) cell
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