FCNU-Net: A Deep Learning Framework Integrating U-Net and Fully Convolutional Networks for Lung Disease Segmentation and Classification Using Chest X-Ray Images

Automated analysis of chest X-ray images has become an important approach for supporting the detection and classification of pulmonary diseases.However, existing deep learning (DL) models often face challenges in accurately capturing complex anatomical structures and preserving detailed boundaries during image segmentation.This study proposes FCNU-Net, a hybrid DL framework that integrates the encoder characteristics of Fully Convolutional Networks (FCNs) with the decoder structure of U-Net for lung disease segmentation and classification from chest X-ray images.The proposed model was evaluated using 876 chest X-ray images collected from Siti Rahmah Islamic Hospital, including normal, pneumonia, and tuberculosis cases.The experimental framework involved image preprocessing, semantic segmentation, and classification based on the modified convolutional architecture.The results demonstrate that FCNU-Net achieved an overall classification accuracy of 96.60%, with a precision of 90.7% and sensitivity of 85.4%.Furthermore, the model obtained an average Intersection over Union (IoU) of 0.8904 and Dice coefficient of 0.9417, indicating effective pixel-level segmentation performance.The proposed framework improves feature representation and boundary localization by combining the strengths of FCN-based dense prediction and U-Net-based spatial reconstruction.These findings suggest that FCNU-Net provides a reliable DL approach for automated lung disease analysis using chest X-ray images and may serve as a potential tool for computer-assisted medical diagnosis.