WHITE BLOOD CELL CANCER CLASSIFICATION USING MODIFIED HONEY BEE OPTIMIZATION ALGORITHM (MHBO) BASED FEATURE SELECTION AND NEURAL NETWORK CLASSIFICATION MODEL

A blood malignancy known as leukemia is defined by the bone marrow’s abnormal and uncontrollably high production of leukocytes, or white blood cells (WBCs). It is possible to identify and diagnose illnesses early by examining photographs of minute blood cells. lately, hematopathologists have been analyzing, detecting, and identifying leukemia kinds in patients utilizing image-processing techniques. Since no specialized equipment is required for lab testing, image detection is a quick and affordable way of detection. Several image processing tools are created for obtaining significant data from medical photos to enhance patient diagnosis. In this research, a feed forward-back propagation neural network framework is presented, and the kind of cancer found in the cells is ultimately predicted. First, the provided photos are subjected to the filtering procedure. The second method is the Fuzzy Inference System (FIS) technique, which is reliable in the face of changing illumination levels and considers the stability levels of color elements to identify edges among color bone marrow microscopic pictures. Third Active contour detection provides an accurate curve of the boundary edge of the cells. And then the background separation is done by Pinched Flow Fractionation (PFF). Then the feature extraction is carried out by the Modified Honey Bee optimization algorithm (MHBO). Finally, the Feed Forward Back Propagation Neural Network (FFBNN) framework-based classifier is proposed for predicting the white blood cells. Based on the findings, this suggested approach created an automated system that allows medical practitioners to accurately diagnose all forms and subtypes of the disease.