Abnormality-aware bone fracture detection and classification using the triple context attention model
Year: 2024
Type: article
Abstract: In this study, a novel approach is introduced for fracture detection in bone x-ray images, introducing the triple context attention model (TCAN) that combines concentrated extensive convolutional segments with an attention mechanism to enhance positional data. The TCAN model significantly improves fracture recognition accuracy while reducing model complexity. Leveraging a diverse dataset, consistently achieving high accuracy levels across various body parts. By addressing, mislabelling issues, and employing a visual attention network (VAN), to refine the model's performance. The TCAN model emerges as a robust, computationally efficient solution, offering a remarkable average accuracy of 97.86%. This study contributes valuable advancements to medical imaging and diagnostics, providing a highly effective tool for skeletal fracture detection.
Authors: Tabassum Nahid Sultana, Asma Parveen
Language: English
Topic: Medical Imaging and Analysis
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403209711