CAE-ResNet18: A Hybrid Deep Learning Framework for Accurate Diagnosis of Developmental Dysplasia of the Hip from Frog-Leg X-Rays

This study aimed to develop and evaluate a deep learning diagnostic model integrating a convolutional autoencoder (CAE) and ResNet18 for the early and accurate diagnosis of developmental dysplasia of the hip (DDH) in children, addressing the subjectivity of traditional methods. This study began with the construction of a CAE network. By introducing a multi-layer compression structure and a Dropout layer, the network was forced to learn the low-dimensional and robust feature representations of the input X-ray images (frog-leg lateral view). The CAE was used to reconstruct the input images and generate enhanced images that highlight abnormal regions. Subsequently, the original and enhanced images were concatenated along the channel dimension to form an information-rich enhanced input. Finally, the pretrained ResNet18 was adopted as the backbone classification network, and its input layer was modified to adapt to multi-channel input to conduct training and classification for the stitched images. Compared to those achieved by the four benchmark models (ResNet18, DarkNet19, AlexNet, and MobileNetV2), the CAE-ResNet18 model achieves excellent performance on the test set. The accuracy, recall rate, and F1-score of the Normal class are 0.9890, 1.0000, and 0.9945, respectively. The accuracy, recall rate, and F1-score of the DDH class were 1.0000, 0.9912, and 0.9956, respectively. Visual analysis shows that the t-SNE visualization of the fully connected layer feature of this model presents a more obvious inter-class separation. The CAE-ResNet18 model effectively leverages both original image information and abnormal features, significantly improving the accuracy and reliability of DDH diagnosis. It provides a potential intelligent tool for clinical auxiliary diagnosis, which may enhance patient prognosis.