Early Stage Detection and Segmentation of Gastric Cancer Using Deep Learning Models

The main cause of the mortality rate of cancer disease throughout the world is gastric cancer. Early detection and segmentation is critical for improving prediction and treatment results. This study observes current improvements in deep learning models for early-stage detection and segmentation of gastric cancer, methodologies, and clinical implications. A hybrid architecture of CNN which are transformer-based model is illustrated in this paper. The architecture highlights the efficiency of analyzing the image quality of histopathological and endoscopic images. To enhance model performance different techniques are discussed like attention mechanisms and data augmentation strategies. Additionally, different challenges are addressed here along with their solution like transfer learning and XAI framework. The outcomes show that the deep learning model achieves higher improvement in sensitivity, specificity, and accuracy as compared to other traditional models. The advancement illustrates the potential for assimilating AI-driven tools in clinical workflow. The studied literature emphasizes the utilization of progressive collaboration between AI Clinicians and research for innovative solutions that will lead to robust healthcare benefits.