An IoMT-Based Surgical Monitoring System for Automated Image Synthesis and Segmentation Using Reinforcement Learning and DCGANs

This work introduces an IoMT surgical monitoring system and automation of picture synthesis and segmentation performed by Deep Convolutional Generative Adversarial Networks (DCGANs) as well as Reinforcement Learning (RL). It augments data augmentation for tissue segmentation and real-time tool control optimization which enhances surgical precision. Our ultimate aim, is to develop an automatic IoMT-driven real-time surgical monitoring and control system using DCGANs and Reinforcement Learning. It improves tissue segmentation and tool accuracy by synthesizing images and doing a lot of preprocessing. The system encompasses GrabCut segmentation, bilateral filtering for pre-processing, SIFT feature extraction and SVM classification. DCGANs are used to generate synthetic surgical images, and Reinforcement Learning with real-world feedback is employed as the tool control portion of this correction. These results show the proposed method achieves better tissue segmentation and surgical tool control with a precision of 98.75%, recall rate of 97.90%, and accuracy of 99.13%. Providing real-time decision support in complex surgical interventions, our IoMT-enabled system boosts tissue segmentation, tool guidance and consequently the overall precision of surgeries.