<![CDATA[Gastro-esophageal reflux disease (GERD) is a widespread condition that often leads to severe complications, including esophagitis, which significantly affects patient health and quality of life. While endoscopy is the gold standard for diagnosing esophagitis, its reliance on specialized equipment and trained professionals can limit accessibility and efficiency. This study introduces an innovative approach to diagnosing esophagitis by leveraging Convolutional Neural Networks (CNN) for automated classification of endoscopic images. By utilizing the Kvasir dataset, which includes a comprehensive collection of gastrointestinal endoscopy images, the model is trained to distinguish between esophagitis and normal-Z-line conditions with remarkable accuracy. The CNN model achieved outstanding results, with an accuracy of 96.04%, precision of 98.94%, recall of 93.00%, and an F1-score of 95.88%, demonstrating its potential to outperform traditional diagnostic methods. These findings underscore the ability of CNN to not only enhance diagnostic precision but also to reduce human error, making the process faster, more reliable, and more accessible. This research contributes to the growing body of work in medical image analysis, suggesting that CNN-based models can transform clinical practices by supporting timely, accurate diagnoses while alleviating the burden on medical professionals. The integration of deep learning in this domain holds the promise of advancing healthcare accessibility and efficiency globally]]>
Copyrights © 2025
