A REVIEW ON WHITE BLOOD CELL IMAGE ANALYSIS FOR LEUKEMIA DISEASE CLASSIFICATION USING DEEP LEARNING TECHNIQUES - I.Vinurajan; Dr. K. P. Sanal Kumar; Dr. S. Anu H Nair (International Engineering Journal For Research & Development, 2021)
Blood cancer is one of the most common and dangerous cancers. Leukemia is a form of blood cancer caused by uncontrolled and abnormal White Blood Cell (WBC) production in the bone marrow. Early diagnosis of leukemia, gives the chance to cure cancer with the right treatment. Counting the amount of white and red blood cells (RBC) is one approach to detect leukemia. A specific equipment known as a haemocytometer is used to count white blood cells and red blood cells in the traditional method. These tests are time-consuming and extremely complicated, resulting in misclassification. The detection of cancer cells can also be enhanced by image processing of microscopic images, which is inexpensive due to the circuitry's simplicity. Several researchers have discovered cancer cells, but they failed to examine all criteria at the same time, such as image enhancement, noise reduction, image identification, and so on, resulting in a reduction in accuracy. The purpose and aim of this study is to develop a new system that takes into account all of the above factors. To detect the existence of cancer and cancer stages, the process includes different stages including image acquisition, image pre-processing, image segmentation, edge detection, and feature extraction. The paper helps in measuring the number of white blood cells and red blood cells, as well as their average cell sizes (regular/irregular), which can be used to detect the existence of cancer.