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Oral cancer as a global health problem becomes more difficult to deal with, especially since developing countries are the hardest hit due to massive rates of mortality and morbidity. Detecting cancer in the early stage and providing an accurate diagnosis is important in improving the outcomes and causing less damage to the patients. The medical technology, particularly in the areas of deep learning, optimization algorithms, and imaging techniques, has begun a new paradigm in oral cancer diagnosing and detecting methodologies. Specialized algorithms such as the Recombination-Based Improved Population Optimization Parallel Covariance Matrix Adaptation Evolution Strategy (RB-IPOP CMA-ES) allow for better accuracy of deep learning models, as this enhances the performance of developing models for early diagnosis of oral cancer. A faithful system of devices with accessible point-of-care screening mechanisms, deployed with neural network classification mechanisms, might become a competitive assurance, especially in resource-deficient areas. For example, skills including Recursive Mean-Separate Histogram Equalization (RMSHE) come in handy in enhancing the resolution of the images and clarity which eventually leads to more accurate lesion identification. Trial-and-error type of survival prediction models, for instance, DeepSurv, offer superior results to conventional approaches and give medical experts the possibility to develop individualized treatment methods. Nevertheless, some obstacles compress the path of implementation. Through the synergy of scientists, clinicians, and policymakers, complexities in the utilization of high-tech solutions to the global effort in oral cancer can be eliminated and access can be paved to better health treatments worldwide. |
