Abstract: Accurate and timely diagnosis of gastrointestinal (GI) diseases is essential for effective treatment and improved patient outcomes. Endoscopy is a key diagnostic tool that provides direct visualization of the GI tract, but manual interpretation of endoscopic images is subject to human error, fatigue, and inter-observer variability. To address these challenges, this research explores the application of deep learning techniques for automated disease identification using endoscopic images. Leveraging convolutional neural networks (CNNs), the proposed approach aims to classify and detect abnormalities such as ulcers, polyps, and early-stage cancers with high accuracy. The model is trained and validated on a diverse dataset of annotated endoscopic images to ensure robustness and generalization. Experimental results demonstrate the effectiveness of the deep learning framework in enhancing diagnostic precision, reducing workload for clinicians, and supporting real-time decision-making in clinical settings. This study highlights the potential of AI-driven tools in transforming endoscopic diagnostics and improving the quality of healthcare delivery.
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DOI:
10.17148/IJARCCE.2025.14558
