Machine Learning Based Blood Cell Categorization in Smear Images

One of the most important tasks in medical diagnostics is the categorization of blood cells from microscopic blood smear images, which helps identify different blood illnesses. This work investigates the use of machine learning approaches for automated image-based blood cell categorization into red blood cells (erythrocytes) and white blood cells (leukocytes). Preprocessing the blood smear pictures to improve contrast and eliminate noise is part of the suggested technique. The important properties, such shape, texture, and color information, are then extracted from the photos using feature extraction algorithms. These characteristics are used as inputs by a number of machine learning methods, such as decision trees, convolutional neural networks, and support vector machines (SVMs). This study advances the creation of automated systems. By advancing automated blood cell categorization systems, this study eventually helps medical personnel identify and track diseases more accurately and efficiently. It also has potential uses in pathology analysis, clinical diagnostics, and medical research.