Develop A Hybrid Improved Weighed Pigeon Optimization with Faster Mask Recurrent Convolutional Neural Network to Classify and Detect Bone Fracture

This research proposes a novel approach for bone fracture classification and detection utilizing a hybrid Improved Weighed Pigeon Optimization (IWPO) algorithm coupled with a Faster Mask Recurrent Convolutional Neural Network (FMRCNN). The IWPO algorithm, an improved method of the traditional Pigeon Optimization Algorithm (POA), introduces weighted factors to achieve a dynamic adjustment of the search process during operation to increase the convergence speed and accuracy of the solutions. The FMRCNN architecture, an evolution of classic CNN models, relies on recurrent links and an effective mask approach for better feature retrieval and positioning capabilities-the LOFAR observations reveal no potential specific behavior processes. IWPO and FMRCNN are hybridized to foster joint efforts of metaheuristic optimization and deep learning methods to optimize the network for bone fracture classification and detection tasks. The experimental results show that the proposed method outperforms the traditional methods in terms of accuracy, efficiency, and robustness in bone fracture diagnosis based on the medical imaging data. This work advances current methods in medical image analysis, providing a potential framework for automated fracture diagnosis and clinical decision support systems.