Securing Privacy in the Metaverse: Advancing Biomedical Image Analysis Through Federated Learning

Literature Review

In the literature review, we first discuss (Messaoudi et al., 2023) this paper showed how deep learning can help physicians diagnose and treat patients with greater accuracy. In a separate context, (Budd et al., 2021) addressed the issue of data scarcity in medical imaging. Their study on the application of transfer learning to prostate cancer detection showed how deep models that were previously trained could be modified to significantly improve performance when data was scarce. This approach and this methodology (Karimi et. al., 2020) have opened up new possibilities for the application of deep learning in healthcare settings despite resource constraints. Biological image processing holds immense potential for deep learning, but there are several challenges to overcome. Two significant issues are the interpretability of deep learning models (Mahapatra et. al., 2022) and the need for (Karimi et. al., 2020) substantial annotated datasets. Also this paper (Mahmood et. al., 2018) discussed how deep learning has the potential to transform healthcare, focusing on personalized medicine and early disease detection. This paper (Lilhore et. al., 2023) highlights the potential of deep learning to improve patient care and provide tailored medicinal solutions. Researchers are increasingly concentrating on overcoming challenges and exploring potential, such as multimodal data fusion (Lilhore et. al., 2024), real-time image processing (Lilhore et. al., 2023), and the integration of 3D imaging (Lilhore et. al., 2022), in the operating room, to enhance healthcare results. The study Deep Learning in Medical Image Analysis (Lilhore et. al., 2020), looks at how enhancing the procedures associated with diagnosis and treatment could completely transform the medical field. The authors stress the necessity for large datasets, interpretability, and validation to emphasize the difficulties involved in creating and deploying CAD or AI solutions in clinical contexts. In order to detect or classify lesions, the study (Onyema et. al., 2023) contrasts deep learning with traditional methods or radiologists. It also discusses the possible advantages of using deep learning to medical image analysis for computer-aided diagnosis (CAD). Our chapter concludes by summarizing the state-of-the-art in deep learning for the interpretation of medical images and highlighting the prospects for further research and improvement in this area.