A New Approach for Leukemia Identification based on Cepstral Analysis and Wavelet Transform
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2017, Vol 8, Issue 7
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.
Authors and Affiliations
Amira Samy Talaat Abou Taleb, Amir F. Atiya
Method for Extracting Product Information from TV Commercial
Television (TV) Commercial program contains important product information that displayed only in seconds. People who need that information has no insufficient time for noted it, even just for reading that information. Th...
Proposed an Adaptive Bitrate Algorithm based on Measuring Bandwidth and Video Buffer Occupancy for Providing Smoothly Video Streaming
Dynamic adaptive streaming via HTTP (DASH) has been popular disseminated over the Internet especially under the circumstances of the time varying network, which it is currently the most challenging for providing smoothly...
Techniques, Tools and Applications of Graph Analytic
Graphs have acute significance because of poly-tropic nature and have wide spread real world big data appli-cations, e.g., search engines, social media, knowledge discovery, network systems, etc. Major challenge is to de...
Arabic Text Categorization using Machine Learning Approaches
Arabic Text categorization is considered one of the severe problems in classification using machine learning algorithms. Achieving high accuracy in Arabic text categorization depends on the preprocessing techniques used...
Data Mining in Education
Data mining techniques are used to extract useful knowledge from raw data. The extracted knowledge is valuable and significantly affects the decision maker. Educational data mining (EDM) is a method for extracting useful...