Advancing Hematological Analysis: A Challenges and Solutions in Deep Learning Based WBC Classification

Abstract

For diagnosis of hematological diseases, the WBC plays a important role, they not only reveal infections but also related diseases, which makes it perfect biomarkers to get more information about the immune system. In our proposed research work, we had done study of most important component of WBC using deep learning approaches and shown the comparative study how it affects hematology diagnosis. It also summarizes new forms of deep learning, such as multi-modal fusion, self-supervised learning, federated learning, and lightweight AI for real-time diagnostics. The focus is on improved model generalization, preserving privacy, and clinically applicable diagnostics. In contrast to existing surveys, this paper bridges AI progress to real-world medical unmet needs, with the intent to inform researchers and practitioners about avenues to pursue when developing robust, interpretable, and scalable AI-based hematological analysis models, which together will lead to improved diagnostic workflows and ultimately better patient outcomes.