Evaluating Deep Learning Approaches for Lymph Node Classification and Gastric Cancer Segmentation

The transformative tool in the field of medical imaging has been emerged by Deep learning which is going to offer an unprecedented advancements in the detection of disease and segmentation. Gastric cancer is a foremost cause of cancer-related death and lymph node classification play crucial roles in the accurate staging and planning of treatment. This research conducts a comparative analysis of deep learning models, which includes U-Net, DeepLabV3, ResNet-50, and Swin-UNet, to calculate their efficiency in the detection of gastric cancer and segmentation of lymph node by using the CAMELYON16/17 dataset for lymph node analysis and the TCGA-STAD dataset for detection of gastric cancer. These models are weighed based on five different critical evaluation parameters. The discoveries highpoint the higher performance of Swin-UNet in segmentation tasks, achieving the maximum IoU scores through both datasets, whereas ResNet-50 outshines in classification accuracy. The rsearch highlights the challenges of managing imbalanced datasets and the necessity for computational efficiency in the application of clinic. Also, statistical analysis exposes substantial differences in model performance, highlighting the significance of selecting task-specific architectures. By giving a complete comparison, this research contributes to the thoughtful of deep learning's importance in the medical diagnostics and its limitations. The addition of innovative models like Swin-UNet demonstrates capacity in improving diagnostic workflows, especially in resource-constrained settings. Future research should emphasis on enhancing model interpretability and concntrating dataset variability to ensure wider applicability. This comparative research goals to guide researchers and clinicians in choosing optimal DL models for detection of gastric cancer and analysis of lymph node.