HP-ResNeXt: Hybrid Pyramid ResNeXt for Detection of Developmental Dysplasia of the Hip in X-ray Image

Abstract

Developmental Dysplasia of the Hip (DDH) is a disease which affects newborn babies and young children. In DDH, the acetabulum may be shallow, or the femoral head may not fit correctly, which causes dislocation or instability of the hip joint. The early detection of DDH failed while the symptoms were mild, which led to delayed treatment and caused severe complications. Thus, the Hybrid Pyramid ResNeXt (HP-ResNeXt) is developed for detecting DDH using hip X-radiation (X-ray) images. Hip X-ray images are sourced from a database, and unwanted noise is removed through a Gaussian Adaptive Bilateral Filter (GABF). Then, a noise-free image is passed to the misshapen pelvis landmark detection phase, where Pyramid Non-local UNet (PN-UNet) is used to identify the affected pelvis region. Entropy-based Local Neighborhood Difference Pattern (LNDP) features, and Gray Level Co-Occurrence Matrix (GLCM) are extracted. Finally, the HP-ResNeXt method is applied for DDH detection, which integrates the advantages of Pyramid Network (PyramidNet) and ResNeXt. The newly introduced HP-ResNeXt approach achieved a True Positive Rate (TPR) of 93.272%, a True Negative Rate (TNR) of 92.567%, and an accuracy of 92.588% with a K-value of 8.