Empowering Rural and Suburban Dental Care with Open-Source Image Processing

The research paper aims to determine dental patient care in suburban and rural areas through open-source image-processing modules. Therefore, the architecture of CNN is discussed according to the demand of the study. Moreover, implementing a particularly trained CNN model with different input data distorts the result. It was found that fixed architecture for of CNN model provides a unique nature to the implication. For instance, in every situation, the algorithms of a CNN model are required to be trained for examining input data. Furthermore, it was found that CNN architecture is subjective to the field of implication. Therefore, the effectiveness of the model helps to produce a relevant and relatable outcome. Open-source imaging processes, associated with AI technologies and advanced image processing hardware and software, can transform dental patient care in rural and suburban domains. Open-source image processing modules have become a necessary technology for their various importance. These technologies can bridge the gap in dental services, by enhancing accuracy, accessibility, and efficiency.