Determination and categorization of Red Blood Cells by Computerized framework for diagnosing disorders in the blood

Abstract

The determination and categorization of red blood cells (RBCs) from microscopic pictures is a critical step in the diagnosis of sickle cell disease (SCD). Traditionally, such procedures are performed manually by pathologists using a light microscope. Furthermore, manual visual evaluation is a time-consuming operation that relies on subjective judgment, resulting in variations in RBC recognition and counts. Mature If there is a blood problem, RBCs suffer morphological alterations. There are both automated and manual systems available on the market for counting the number of RBCs. Manual counting entails collecting blood cells with a Hemocytometer. The traditional procedure of exposing the smear below a microscope and physically measuring the cells yields inaccurate findings, putting clinical laboratory staff under stress. Automatic counters are incapable of detecting aberrant cell. The computer-aided method will assist in achieving accurate outcomes in minimum time. In this study presents an image processing method for separating red blood cells from several other blood products. Its goal is to analyze and interpret blood smear images to aid in the categorizing of red blood cells across 11 categories. The WBCs are extracted from the image using the K-Medoids technique, that is resistant to exterior disturbance. Granulometric assessment has been used to distinguish between red and WBCs. Feature extraction is used to obtain important features that aid in categorization. The categorization outcomes aid in a rapid diagnosis of disorders such as Normochromic, Iron Deficiency, Hypochromic, Sickle Cell, and Megaloblastic.