Published Paper Details:

BLOOD tests are frequently employed to evaluate human health. One of the most straightforward blood tests is to quantify and identify the blood cell types. A complete blood count (CBC) is primarily a measure of these cellular components and is one of the most routinely ordered blood tests by clinicians. CBCs, especially white blood cell (WBC) count, provide physicians with key information valuable for diagnosing many different disease states including anemia, leukemia, autoimmune disorders, fungal, and bacterial infections as well as Recognition and inspection of white blood cells in peripheral blood can assist hematologists in diagnosing many diseases such as AIDS, Leukemia, and blood cancer. Thus, this process is assumed as one of the most prominent steps in the hematological procedure. This Purposed work purposes the tasks of extracting, classifying and segmenting the Five Types of White Blood Cells using more efficient supervised learning approaches for more accurate and computationally efficient segmentation.