Navigating the Evolving Field of Blood Cancer Detection: Insights from Traditional Methods to Cutting-Edge Technologies

Abstract

The complexity and heterogeneity of blood cancers make detection and classification difficult in the medical field. This survey paper examines the various techniques and methodologies used to detect and classify BCs. The paper begins with an overview of the multiple types of blood cancers, such as leukemia, lymphoma, and myeloma. Then, it delves into traditional diagnostic methods like blood tests, bone marrow biopsy, and imaging techniques. The paper also investigates the advancements in molecular diagnostics, such as genomic profiling and liquid biopsy, in understanding BC biology and personalised treatment strategies. It also looks at machine learning and artificial intelligence approaches to BC detection, segmentation and classification, offering insights into current challenges, emerging trends, and future directions in this critical area of oncology research.