Review on Feature Extraction and Classification of WBC in Bone Marrow for Disease Diagnosis

Abstract

In this paper, we make a review on different features and classification methods for white blood cells in bone marrow for disease diagnosis. A new set of features based on the wavelet and Radon transform to bone marrow blood cell differential classification solution. The transform based novel features are the coefficient values of decomposition for horizontal, vertical & diagonal factors with two levels. the factors where the coefficient values are of six in numbers the first and second level coefficient method to the classifiers by the desired output using a previous information of the number of coefficients as a samples in each class. Artificial neural networks Levenberg-Marguardt (LM) back propagation algorithm for validation is applied in the experiments. The results are used in various disease diagnoses.