Theoretical advancements in Deep Learning for Hematological Cancer Diagnosis: Proposing a New Framework and Mathematical Model

Abstract

The rising prevalence of hematological malignancies, such as leukemia, in India underscores the critical need for enhanced diagnostic methods that enable early detection and treatment. This study embarks on a thorough examination of existing deep learning (DL)¹ methodologies applied to the diagnosis of hematological cancers, highlighting pivotal advancements and identifying prevailing gaps in current approaches. Without relying on actual datasets, our research synthesizes findings from extensive literature to propose a robust theoretical framework and a comprehensive mathematical model designed to enhance diagnostic accuracy. The proposed framework leverages advanced machine learning techniques, including enhanced Generative Adversarial Networks (GANs)² and Convolutional Neural Networks (CNNs) via sophisticated transfer learning processes. We introduce novel segmentation and classification algorithms that address specific challenges such as overlapping nuclei and morphological heterogeneity. The integration of Explainable AI (XAI) and principles of federated learning in our model underscores our commitment to maintaining transparency and safeguarding data privacy in clinical applications. By theoretical alignment and mathematical rigor, our proposed model aims to set a new benchmark in the diagnostic procedures of hematological malignancies, offering a scalable and adaptable solution that can be empirically validated in future research.