Image Processing Based Esophagus Auto Countering using Resnet-50 for Cancer Detection

The early detection of esophageal cancer is crucial for improving patient outcomes. Traditional diagnostic techniques rely heavily on manual assessment, which can be time-consuming and prone to human error. Recent advancements in deep learning and image processing have enabled automated detection methods with higher accuracy. This project focuses on developing a deep learning-based segmentation model using ResNet-50 integrated into a U-Net architecture for esophageal cancer detection. The methodology includes data collection, augmentation, model training, and evaluation using standard performance metrics such as the Dice Coefficient and Intersection over Union (IoU). The proposed model demonstrates significant improvements in segmentation accuracy compared to traditional approaches. Experimental results indicate that the ResNet-50-based U-Net achieves high precision in detecting esophageal cancer, making it a reliable tool for medical practitioners. This research contributes to the advancement of automated medical imaging solutions and has the potential for real-world clinical applications.