Summary of my research methodology

Every good research has a methodology it follows and my research is not different. Currently I have been working on a beautiful research for my dissertation and I have been able to clearly lay down the steps so that anyone who follows it will arrive at the result I have and even better based on the advancement in technology.

This chapter outlines a rigorous methodology for developing and evaluating a multi-modal federated learning framework for uterine cancer classification. Grounded in a post-positivist stance and realist ontology, the research emphasizes privacy, clinical relevance, and scientific rigor. The design follows a quantitative, experimental, multi-phase approach, progressing systematically from problem formulation to results analysis, supported by a clear flowchart. Model development features modular branches for each modality, multiple fusion strategies (baseline, attention, weighted), and a federated learning framework enabling privacy-preserving distributed training. Data collection relies on TCGA as the primary source, with robust preprocessing pipelines, quality assurance, and validation across modalities. Ethical considerations include adherence to TCGA policies, fairness, transparency, accountability, and privacy-preserving principles. Limitations are acknowledged, such as dataset constraints and generalizability challenges, while theoretical implications highlight contributions to multi-modal learning and federated learning. Overall, the methodology establishes a reproducible, ethically sound foundation for implementation and evaluation in Chapter 4.