Fracture Detection in X-Ray Images Using Whale Optimization Algorithm-Enhanced Bi-Directional LSTM Networks

Rapid and reliable fracture detection in X-ray images is essential for medical diagnosis. This research offers a hybrid deep learning strategy that leverages the Whale Optimization Algorithm (WOA) to choose the most significant features from AlexNet and ResNet50. WOA enhances feature selection by searching for relevant attributes. To identify fractures, a Bi-directional Long Short-Term Memory (Bi-LSTM) network is employed. The proposed hybrid fracture diagnosis method beats existing methods with 96.86% accuracy. Integration of optimization approaches with powerful deep-learning architectures may improve medical imaging diagnosis accuracy.