VGG-16- Based Deep Learning Architecture for Automated Chest X-Ray Diagnosis: Improving Clinical Accuracy and Reducing Environmental Footprint

Interpretation of Chest X-rays is a significant aspect of clinical diagnosis, yet the traditional manual reporting system is time-consuming, cumbersome, and prone to inter-observer variability. As the global demand for fast and high-quality diagnostic services, particularly in resource-limited settings, automated platforms are more critical than ever. This article presents an AI-based automated reporting system for chest X-rays based on deep learning algorithms to enhance diagnostic quality and support sustainable healthcare delivery. This study compared and trained five of the newest convolutional neural network (CNN) models—InceptionV3, EfficientNet, DenseNet, ConvNeXt, and VGG-16—on the NIH Chest X-ray dataset consisting of 10,000 front-view images and their corresponding diagnostic labels. The models were compared for accuracy, precision, recall, F1 score, and area under the curve (AUC). All the models were of high quality, but among them, VGG-16 was the best with highest accuracy of 93.88% and was the most stable in producing clinically applicable diagnostic reports. In previous studies,earlier models such as ResNet, U-Net with DeCovNet, VGG-19, and initial implementations of VGG-16 have faced challenges such as overfitting, task restriction, and lack of fine-tuning. In contrast, our VGG-16 model reduces such loopholes via accurate training and enhanced multi-label classification. Not only does the process enhance the effectiveness of radiology processes but also promotes sustainable healthcare approaches. The above system not only increases the productivity and reproducibility of radiology reporting but also paves the way for paperless, digital healthcare operations. By needing fewer printed reports, the system helps in adopting green initiatives for medical diagnosis.