Enhancing Gastric Cancer Diagnosis Through Ensemble Learning for Medical Image Analysis

Even with the present state of medical imaging technology, accurately and quickly detecting stomach cancer is still a major difficulty. Even though many detection techniques have advanced, current research noticeably lacks a cohesive strategy that integrates multiple models for reliable detection. In response, this research uses stacking ensembling methods to present a unique paradigm for stomach cancer diagnosis. Our goal is to increase the accuracy of stomach cancer diagnosis by using the power of many sophisticated semantic segmentation models, such as U-Net, DeepLab, and SegNet, in order to overcome the current constraints. We comprehensively examine the effectiveness of the various models and the ensemble technique via a rigorous review of important performance measures, such as accuracy (Ensemble: 97.8%, U-Net: 95.8%, DeepLab: 93.3%, SegNet: 92.5%), F1 score, ROC-AUC, precision, recall, and IoU. According to our findings, the ensemble model—which is enabled by a Random Forest Classifier—performs better than the individual models in identifying stomach cancer. By offering a complete and accurate method for the accurate and dependable identification of stomach cancer, this research contributes significantly to the area of medical image analysis and facilitates early diagnosis and better patient outcomes.