Esophageal Abnormality Detection System

Early recognition of esophageal pathological conditions, especially malignant tissues, is critically important for improving therapeutic outcomes and extending patient survival rates. This study introduces a novel AI-based web platform that utilizes advanced machine learning techniques for automatic detection of esophageal diseases through digital medical image analysis. The developed system implements a Convolutional Neural Network framework, specifically trained using diagnostic imaging data, to differentiate between healthy and pathological tissue characteristics. The web-enabled platform incorporates complete user verification systems, instant image analysis, and detailed output presentation with accuracy confidence scores. The proposed solution attained 94.7% precision in identifying esophageal disorders, indicating significant promise for supporting medical professionals in timely diagnosis. The system integrates contemporary web development technologies with adaptive interface layouts, protected data management, and healthcare privacy standards-compliant processing methods. This research enhances medical image analysis capabilities and offers a viable solution for medical institutions to improve their diagnostic accuracy.