DETECTION OF LEUKEMIA IN MICROSCOPIC WHITE BLOOD CELL IMAGES USING GAUSSIAN FEATURE CONVOLUTIONAL VISUAL RECOGNITION ALGORITHM

Abstract

Cancer detection has always been a challenge in the diagnosis and treatment plan for hematological diseases. Manual identification of cancers from microscopic images is necessary. It depends on other factors, including the lack of microscopic images as their knowledge and precise measurement of specific usual or cancer expert classifications. Automated identification of cancer cells from microscopic section images and helps to improve the problem mentioned above, based on biological interpretation. Detection of blood cancer cells by a patient's blood mark microscopic examination using different techniques of image processing. White blood cell cancer images and their essential three-stage there are an enhancement, segmentation, and classification. The stage of image processing to achieve more quality and accuracy in detecting blood cancers. Approaching the conventional segmentation and counting of blood cells is considered as an essential step that helps to extract features to diagnose diseases like leukemia. The image analysis will allow hematologist experts to perform faster and more accurately. In this work, the image recognition problem of White Blood Cells (WBC) cancer is investigated. Different types of white blood cells are classified using a Gaussian Feature Convolutional Visual Recognition (GFCVR).The most important features or segments of these blood cells are provided as input microscopic images to the neural network. The consequences of this analysis are mainly considering the identification of blood cancer affected region with feature extraction for specific white blood cell images. In this proposed approach pre-processing the image, classifies multi-level clustering to find the affected area in blood cells. In this proposed simulation, results in time complexity, accuracy, recall, perception, false classification ratio gives better results compare with other existing methods.