Published Paper Details:

Leukemia, a widespread and life-threatening cancer that affects people of all ages, is a global health problem. This disorder predominantly affects White Blood Cells (WBCs), altering bone marrow and blood and causing immature lymphocyte proliferation. The accurate and prompt identification of leukemia is critical for successful treatment and increased survival rates. Currently, the diagnosis is based on manual examination of blood samples from microscopic pictures, a slow, time-consuming technique with inadequate accuracy. Furthermore, the visual resemblance between leukemic and normal cells under a microscope adds to the detection problem. Recent years have witnessed the emergence of Convolutional Neural Network (CNN)-based deep learning algorithms, setting new benchmarks in image classification. However, chances to improve their efficacy, learning processes, and overall performance remain, notably in the field of leukemia diagnosis. In this detailed analysis, we dig into several methodologies previously used in the field of blood cancer detection. In addition, we highlight the field by displaying benchmark datasets often used in leukemia detection studies. We want to clarify the intricacies and complexity within this domain through comparative study, thereby driving advancement in leukemia diagnosis and treatment.