Multi‐Channel Fusion Residual Network for Robust Bone Fracture Classification From Radiographs
T Sivapriya, K. R. Sri Preethaa, Yuvaraj Natarajan, M. Shyamala Devi · IET Image Processing · 2026
ABSTRACT Accurate bone fracture classification from radiographs is hindered by low fracture visibility, imaging artefacts and high intra‐class similarity. To overcome this, multi‐channel fusion residual network (MFResNet18) is proposed that integrates a multi‐modal channel (MMC) filter with a multi‐path early feature extraction scheme to enrich fracture relevant features before deep inference. The MMC filter transforms each fracture image into five complementary channels as the original image, the Frangi filter for fracture line enhancement, the Difference of Gaussian (DoG) edge map, mid‐frequency wavelet decomposition and the bone mask for contextual details. These channels are processed through three parallel shallow CNN paths. Path 1 handles pathological features with the original image and the Frangi filter, path 2 processes wavelet features having DoG and wavelet, and path 3 processes the anatomical features with the bone mask as an attention channel. The outputs are fused through convolution in a feature fusion layer, which adaptively learns inter‐modal features while preserving spatial fidelity. The fused feature map is then propagated through a modified MFResNet18 backbone for hierarchical residual learning. Experimental results with the bone fracture dataset demonstrate that MF‐ResNet18 achieves 99.72% classification accuracy, significantly outperforming conventional ResNet18 and other existing models. The integration of MMC filtering, multi‐path early specialisation and learnable feature fusion serves as a key novelty of this work that offers a robust, extensible framework for fine‐grained bone fracture classification.