Lightweight Deep Learning with Virtual Reality Visualization for Offline Tumor Segmentation in Rural Environments

Advanced medical imaging has enhanced diagnostic accuracy and patient outcomes. Continued improvement means that the innovation presents significant medical benefits for health services, professionals and patients. However, access and adoption of the technology remain uneven due to the level of digital infrastructure and technical expertise required. The human and technical resources particularly impact rural and resource-constrained settings. These environments often face infrastructural limitations, unreliable connectivity, and restricted computational capacity, hindering equitable access to innovative technologies. In response, this research proposes a novel theoretical framework that integrates lightweight, quantization-enhanced deep learning with immersive offline virtual reality to generate high-fidelity tumor segmentation images tailored for low-resource contexts. This approach facilitates sporadic distant expert consultations, enhances local clinician training, and aligns medical technology deployment with environmental sustainability. While challenges remain in balancing accuracy, computational efficiency, patient acceptance, and regulatory compliance, this framework holds significant promise for advancing scalable, equitable healthcare delivery and diagnostic reliability in underserved settings.