BoneVisionNet: A deep learning approach for the classification of bone tumours from radiographs using a triple fusion...

Diagnosis of bone tumours present numerous challenges due to the complexity of pathology and varying morphologies of bone tumours. Current methods rely on manual techniques that are time-consuming and prone to errors. Hence, there is a need for more accurate and automated methods to assist medical professionals. The proposed work aims to solve this challenge by developing a deep learning-based architecture for bone tumour classification using radiographs. The proposed BoneVisionNet is developed using a combination of three specialized DL networks. The proposed approach leverages Convolution-Enhanced Image Transformer for global feature extraction which is further refined using a Global Context Block (GCB). In parallel, the Attention Boosted Mid-Level Feature Extraction Network (ABMLFE-Net) targets mid-level features and DenseNet-169 focuses on local feature extraction. The feature maps from the ABMLFE-Net and DenseNet-169 are fused using element-wise multiplication and is followed by an Efficient Channel Attention (ECA) layer for feature refinement. The global features that are refined by GCB are concatenated with the enhanced feature maps from the ECA layer, resulting in an refined multi-scale feature map. The BoneVisionNet attained an accuracy of 84.35 % when tested on the BTXRD dataset, outperforming CNN and transformer-based networks for classifying bone tumours from radiographs. To the best of our knowledge, this study represents the first application of a triple-track architecture for the classification of bone tumours from X-ray images. XAI visualisations using Grad-CAM, LIME, and SHAP help to further validate the performance of the model by ensuring transparency in the decision-making process.