Image Processing based Smart, Economical Blood Cancer Identification System

Abstract

By separating young white blood cells from red blood cells, blood leukaemia can be detected with more accuracy and specificity. The only method to determine if someone has a blood problem is to take skin photos, calculate, shade, and measure them. The World Health Organization ranks leukaemia as the sixth most common cause of death globally. For a therapy to be effective and have a positive outcome, infection must be identified and detected early. In this research, it is believed that the cells that are connected to leukaemia would be able to be found and tested. Whether or not immature cells are present, as well as the degree of chronic or severe leukaemia, can be used to categories this illness. By combining morphological methods like region opening, region closing, disintegration, and expansion with histogram levelling and straight difference extending, it is possible to obtain a more even distribution of data points. As a result, the Proposed Method is more efficient than other earlier techniques.