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Gastric cancer (GC) remains a significant global health issue, and convolutional neural networks (CNNs) have shown their high potential for detecting precancerous gastrointestinal (GI) conditions on endoscopic images [1] [2]. Despite the need for high resolution to capture the complexity of GI tissue patterns, the impact of endoscopic image resolution on the performance of these models remains underexplored. This study investigates how different image resolutions affect CNNs classification of intestinal metaplasia (IM) using two datasets with different resolutions and imaging modalities. Our results reveal that the often adopted input resolution of 224×224 pixels does not provide optimal performance for detecting IM, even when using transfer learning from networks pre-trained on images with this resolution. Higher resolutions, such as 512×512, consistently outperform 224 × 224, with notable improvements in F1-scores (e.g., InceptionV3: 94.46% at 512 × 512 vs. 91.49% at 224 × 224). Additionally, our findings indicate that model performance is constrained by the original image quality, underscoring the critical importance of maintaining the higher original image resolutions and quality provided by endoscopes during clinical exams, for the purposes of training and testing CNNs for gastric cancer management.Clinical Relevance- This research highlights the importance of image quality, particularly when endoscopes capture lower-resolution images. Understanding how image resolution impacts diagnostic accuracy can guide clinicians in improving imaging techniques and employing Artificial Intelligence-driven tools effectively for more accurate GC detection and better patient outcomes.