Determination of Anteroposterior and Posteroanterior Imaging Positions on Chest X-Ray Images Using Deep Learning †

Abstract

This study proposes a deep learning framework to classify anteroposterior (AP) and posteroanterior (PA) chest X-ray projections automatically. Multiple convolutional neural networks (CNNs), including ResNet18, ResNet34, ResNet50, DenseNet121, EfficientNetV2-S, and ConvNeXt-Tiny, were utilized. The NIH Chest X-ray Dataset, with 112,120 images, was used with strict patient-wise splitting to prevent data leakage. ResNet34 achieved the highest performance: 99.65% accuracy, 0.9956 F1 score, and 0.9994 ROC-AUC. Grad-CAM visualized model decisions, and expert-reviewed misclassified samples were removed to enhance dataset quality. This methodology highlights the importance of robust preprocessing, model interpretability, and clinical applicability in radiographic view classification tasks.