Analysis of gastric cancer using artificial intelligence: An innovative approach in oncological research. (Innovation & Development in Engineering and Applied Science, 2025)
Gastric cancer (GC) remains one of the leading causes of cancer-related mortality worldwide, primarily due to the challenges of early diagnosis, which often relies on invasive procedures and manual image analysis. This study presents the development of a web-based application powered by artificial intelligence, using convolutional Neural Network for the automatic detection of anomalies in endoscopic images.The methodology was structured into four phases: data collection, preprocessing, model implementation, and system evaluation. The algorithm achieved a classification accuracy of 97.4% and an average analysis time of 0.10 seconds per image, exceeding clinical performance benchmarks.The system proved to be effective, fast, and accessible to healthcare professionals, including those in resource-constrained environments. Its user-friendly interface and centralized database facilitated seamless integration into clinical practice.This research highlights the potential of deep neural networks in supporting early diagnosis of gastric cancer. Future work will focus on validating the tool in real-world clinical settings and exploring additional deep learning architectures to further enhance system performance.
Keywords: Gastric cancer; Artificial Intelligence; Endoscopic images