Bone Fracture Segmentation Using Cascaded Convolutional Neural Networks

In order to effectively treat bone fractures, an appropriate diagnosis must be made quickly after the injury occurs. Medical picture segmentation is an area where deep learning methods, especially Convolutional Neural Networks (CNNs), have made significant strides in recent years. Cascaded Convolutional Neural Networks (C-CNNs) are proposed as a new method for segmenting bone fractures in this research. The cascaded architecture enables the segmentation model to focus on refining the boundaries and intricate details of fractures, leading to improved accuracy and reliability. The first stage of our proposed framework consists of a dedicated CNN designed to detect potential fracture regions within X-ray images. By efficiently identifying the candidate fracture areas, the model reduces the search space for subsequent stages, enhancing computational efficiency. In the second stage, a refined CNN further processes the detected regions to accurately delineate the boundaries of the fractures. The network can learn complicated fracture patterns and nuances seen in the X-ray pictures because to the cascaded architecture that allows for specialized emphasis at each level. We used a large dataset of annotated X-ray images encompassing various fracture kinds and severities to train and assess the proposed C-CNN architecture. In terms of segmentation accuracy, sensitivity, and specificity, our experimental findings show that the cascaded technique excels above typical single-stage CNN models. The suggested technique also shows resilience over a wide range of fracture types, from the most frequent to the rarest, making it excellent for use in the clinic.