Deep learning based automated esophageal cancer detection using medical imaging

Early detection of esophageal cancer significantly improves patient survival rates; However,conventional diagnostic techniques are often invasive, costly, and prone to subjective interpretation by medical experts. This paper presents a deep learning based automated system for esophageal cancer detection using medical imaging modalities such as endoscopic and CT images. The proposed system employs convolutional neural networks (CNNs) to automatically extract meaningful features and perform accurate classification. Image preprocessing techniques such as normalization, noise filtering, data augmentation, and region-based segmentation are applied to enhance image quality and improve model robustness. The performance of the system is evaluated using standard metrics including accuracy, sensitivity, specificity, precision, and F1-score. Experimental results demonstrate that the proposed approach achieves reliable performance, demonstrating its effectiveness as a clinical decision- support tool for early diagnosis, reduced diagnostic workload, and improved screening efficiency in esophageal cancer detection.