Machine learning has emerged as a transformative tool in the medical field, particularly in enhancing diagnostic accuracy and therapeutic decision-making. In the context of gastrointestinal (GI) diseases, its application is reshaping early detection and treatment strategies. Traditional GI diagnostic methods often rely heavily on manual interpretation of endoscopic images, which can be time-consuming and subject to inter-observer variability. This can lead to delays in diagnosis and inconsistent therapeutic outcomes. To address these limitations, we propose a Convolutional Neural Network-based system for Analyzing Endoscopic Images (CNN-AEI), aimed at improving the early detection of gastrointestinal abnormalities. This system automates image analysis using deep learning, enabling real-time assessment with higher precision. The proposed method is implemented to support clinicians by providing accurate, consistent, and rapid diagnostic feedback from endoscopic imagery. Experimental results demonstrate that the CNN-AEI framework significantly improves diagnostic accuracy, sensitivity, and specificity compared to conventional assessment methods. This advancement has the potential to reduce diagnostic errors and support timely therapeutic interventions, ultimately enhancing patient outcomes in GI care.

Machine Learning, Gastrointestinal Diagnosis, Endoscopic Images, Convolutional Neural Networks, Early Detection, Medical Imaging.