A Deep Learning Framework for Timely Bone Fracture Detection and Prevention

• DOI: 10.61356/j.iswa.2024.19673
• Publication Date: 2024-04-03T00:04:59Z
• Authors: Ahed J Alkhatib; Mohamad Alharoun; Areej AlZoubi
• Keywords: Artificial Intelligence in Medicine; Fracture (geology); Artificial intelligence; Biomedical Engineering; Health Informatics; Geology; FOS: Earth and related environmental sciences; FOS: Medical engineering; Computer science; Geotechnical engineering; Engineering; Physical Sciences; Health Sciences; Medicine; Automated Spine Segmentation and Identification

Accurate bone fracture identification remains critical in medical diagnostics, motivating researchers to investigate deep-learning systems address this difficulty. The performance of convolutional neural networks (CNN) and region-based (RCNN) diagnosis is investigated paper. Algorithmic efficacy was using a complete set experiments involving various epochs, batch sizes, optimization techniques (Adam SGD). As discussed the discussion, results continuously highlight superiority RCNN algorithm, which demonstrated amazing across many experimental settings. Algorithms trained Adam optimizer regularly high levels accuracy, precision, recall, F1 score. Nonetheless, effect epoch counts size on variability seen, necessitating careful consideration avoid overfitting ensure generalization. These findings support algorithm selection guided by fine-tuned hyperparameters. algorithm's impressive results, proven its constant superiority, underline potential revolutionize diagnosis. Further research should focus hyperparameter tuning comprehensive validation several datasets, fostering accurate efficient solutions for diagnoses practice.