Detection of oral tumour cells using quantum optics with carbon/graphene dot models

Integrating new technology in medical diagnostics presents significant potential for improving precision and productivity. To detect oral tumor cells, this study presents a unique method that uses carbon/graphene dot (CGD) and a Customized Dual Deep Neural Network (CDDNN), known as CGD-CDDNN. Given their distinct electrical and optical characteristics, graphene quantum dots (GQDs) are used as quantum dots in quantum optics to simulate the behaviour of carbon and graphene. The proposed CDDNN architecture has two routes for processing various characteristics associated with tumor cells and is specifically designed to detect oral tumors. This customized deep-learning model’s goal is to recognize complex relationships and patterns in a variety of data sources. Because the network is dual, information may be processed in parallel, and complimentary aspects can be integrated more quickly. To take advantage of the unique qualities of CGDs, quantum optics concepts are incorporated into the model, offering a quantum-level comprehension of light-matter interactions in the context of oral cancer cell identification. The CGD-CDDNN system is trained on a dataset of labelled pictures of oral cancer cells using quantum-inspired embeddings and sophisticated optimisation methods. The validation of the proposed model demonstrates its effectiveness in detecting oral tumour cells, showcasing superior performance compared to conventional methods. The study emphasises the potential of quantum optics with carbon/graphene dot models and customised dual deep neural networks in revolutionising the landscape of medical imaging and tumour diagnostics. The CGD-CDDNN framework enhances accuracy and provides valuable insights into the quantum-level interactions within biological systems, paving the way for innovative applications in precision medicine.