Abstract: Early and accurate identification of gastrointestinal (GI) diseases is critical for effective treatment and improved patient outcomes. Endoscopy provides high-resolution images of the GI tract, but manual interpretation is time-consuming and prone to human error. This study presents an automated approach for disease identification from endoscopy images using deep learning techniques. A convolutional neural network (CNN) model is trained on a labeled dataset of endoscopic images to classify various gastrointestinal conditions such as ulcers, polyps, esophagitis, and bleeding. The system incorporates image preprocessing, data augmentation, and model optimization to enhance detection accuracy. Experimental results demonstrate the model’s ability to achieve high classification accuracy, offering a reliable tool to assist clinicians in diagnostic decision-making. This approach has the potential to improve diagnostic efficiency, reduce workload on medical professionals, and enable scalable screening in resource-limited settings.
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DOI:
10.17148/IJARCCE.2025.14559
