Implementing Convolution Neural Network Model for Identifying and Detecting Waldenstroms Maculoglobuluminea Cancer
Keywords:
CNN, Cancer Analysis, Cell Images, Waldenstrom Macroglobulinemia, Blood CellsAbstract
This paper has aimed to design and implements a deep learning algorithm for identifying and detecting Waldenstrom Macroglobulinemia (WM) cancer. It affects the blood cells and causes various health problems in the human body. Several earlier research works have proposed clinical methods which can only be understood by medical experts and can not understand by non-medical candidates belonging to other related fields. Some of the researchers have used medical image processing methods for analyzing blood cell images for WM detection, where the accuracy is not satisfactory. This paper proposed a Convolution Neural Network model for analyzing and interpreting cell images for identifying and detecting WM cancer. Since the CNN learns the image deeply and extracts more features the prediction accuracy is high and it outperforms other methods. It provides F1-Score of 93.1% in WM prediction in detecting WM cancers from cell images.
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