Gastric Cancer Detection Using Yolov7 and CNN Based Ensemble Learning Models

This paper explores the integration of You Only Look Once Version 7 (YOLO) v7 algorithm within Convolutional Neural Network (CNN) frameworks for automated analysis and detection of gastric cancer in medical images. YOLO algorithm surpasses existing YOLO versions and object detection models in both accuracy and speed. By integrating YOLOv7 within CNN architectures, enables effective and accurate diagnosis of gastric cancer from gastrointestinal (GI) medical images. Our trained model can integrate into CNN frameworks, enabling end-to-end analysis pipelines for various medical imaging modalities, including endoscopy images. The performance of developed model can be examined on GI related medical image datasets, assessing both the accuracy and computational efficiency of the YOLO-CNN framework. Comparative analysis with existing methods should be performed to know the effectiveness and robustness of our approach in detecting and localizing cancer across GI-related datasets and endoscopy images. Overall, our research showcases the potential of YOLOv7 within CNN frameworks for automated analysis of GI based medical image analysis.