Gastrointestinal diseases have a growing impact on public health, often requiring timely and accurate diagnosis to prevent complications and improve patient outcomes. In this context, artificial intelligence (AI) has emerged as a promising tool to support clinicians in image-based diagnosis. This study presents the design and evaluation of an explainable deep learning system for the automatic detection and classification of gastrointestinal anomalies in colonoscopy images. Using transfer learning and convolutional neural networks, the proposed architecture incorporates a fine-tuned ResNet18 model alongside explainable AI (XAI) methods to ensure both high diagnostic performance and model transparency. The system was trained and validated using the Kvasir dataset, a clinically annotated collection of endoscopic images covering multiple gastrointestinal conditions. Experimental results show that the use of transfer learning significantly improved classification outcomes, with F1-scores exceeding 0.90 for several key categories. A web-based interface was also developed to facilitate clinical adoption, providing visual explanation tools such as heatmaps. These allow healthcare professionals to understand the basis of each prediction, promoting trust and supporting informed decision-making. Overall, the system contributes to more accurate, interpretable, and efficient diagnostic processes in the field of gastrointestinal healthcare.