Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Innovation Information Technology and Application (JINITA)
Dhanar Intan Surya Saputra
Amikom Purwokerto University, Banyumas
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Automated Esophagitis Detection from Endoscopy Using Deep Learning Imam Kharits N; Imam tahyudin; Dhanar Intan Surya Saputra
Journal of Innovation Information Technology and Application (JINITA) Vol 7 No 2 (2025): JINITA, December 2025
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v7i2.2835

Abstract

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
Co-Authors Imam Kharits N Imam tahyudin