Detection and Classification of Leukaemia using Artificial Intelligence

Leukemia is a kind of cancer that affects the bone marrow and blood. It is typically diagnosed through a combination of symptoms, examination, and laboratory tests, such as the Complete Blood Count (CBC), which measures the types and counts of white blood cells, red blood cells, and platelets, among other things. The goal of this study was to create a diagnostic model for leukaemia using a CBC dataset that collected. The model was created by training machine learning techniques on a preprocessed dataset. Following that, accuracy, precision, and recall metrics were used to assess the model. The benefits of using a diagnostic model for leukaemia based on a CBC dataset include speed, efficiency, high performance, low cost, and automation. The advantages and limitations of these techniques will be evaluated and consolidate our findings to provide guidance for future research in this area.