Comparative study on Early esophageal cancer detection

Abstract - Esophageal cancer (EC) is a rare but serious health condition due to its late diagnosis and high fatality rates. Its early detection greatly enhances patient outcomes, and recent advances in artificial intelligence (AI) introduce new possibilities in diagnosis. This research explores deep-learning models for early EC detection based on high-resolution endoscopic images. We compared logistic regression, decision-tree and random forest methods as conventional machine learning methods with deep learning models like convolutional neural networks (CNNs) and U-Net architectures to separate cancerous from non-cancerous esophageal tissues. Annotated endoscopic images were used as the dataset, with models assessed in terms of accuracy, sensitivity, and specificity. Pilot results indicate that deep learning, particularly CNNs, performs better than conventional machine learning. These results indicate AI-based detection combined with routine procedures can enhance early diagnosis, lower errors, and enhance EC patient survival. Research in the future should improve model robustness and establish efficacy across a wide population. Key Words: Logistic Regression, Decision Tree, Random Forest, U-Net architectures, CNN, Esophageal Cancer, Machine Learning, Early Detection