Application of convolutional neural networks for automated segmentation and classification in esophageal diseases

Objective To develop a convolutional neural network (CNN) framework for the automated segmentation and classification of esophageal lesions in endoscopic images. Methods (1) Lesion localization was performed using a Region-based Convolutional Neural Network (R-CNN). (2) A dual-stream Esophageal Lesion Network (ELNet) was developed to classify images into four diagnostic categories. (3) Lesion segmentation was carried out using an ensemble of three U-Net architectures. Results The dual-stream ELNet achieved a classification accuracy of 92.14%, with 97.1% specificity and 88.74% sensitivity. The segmentation module based on U-Net attained an overall accuracy of 95.54% and a lesion segmentation sensitivity of 82.89%. The dual-stream ELNet consistently outperformed single-stream baseline networks, and the integrated segmentation-with-classification architecture demonstrated enhanced adaptability across diverse lesion types. Conclusion The proposed CNN framework enables accurate, robust, and simultaneous classification and segmentation of esophageal endoscopic lesions, exhibiting high performance and clinical potential.