Optimizing Osteoporosis Detection with Cascaded Convolutional Neural Network and Real-Coded Genetic Algorithm
Optimizing Osteoporosis Detection with Cascaded Convolutional Neural Network and Real-Coded Genetic Algorithm

Summary/Abstract: Background: Osteoporosis is a condition characterized by bones that are porous and brittle, increasing the risk of fractures. It is often asymptomatic until substantial harm develops, making it crucial to treat at the onset of the disorder. Objective: This study aims to develop a practical, in-depth and adaptable framework utilizing constructed clinical and demographic datasets for the early detection of osteoporosis. Methods: We design a cascade convolutional neural network with adaptive weight fusion and fine-tune it with a real-coded genetic algorithm. An anonymized clinical and demographic record publicly available bone mineral density dataset was used. Missing data identification, normalization and encoding of categorical variables were key diagnostic steps. The training subset consisted of 70% of the dataset, while the remaining 30% was used for testing. Results: The predictive capability of the proposed model is demonstrated by utilizing two datasets. Dataset 1 is used for training and testing, achieving a classification accuracy of 99.5%, precision of 98.7%, recall of 99.0% and AUC-ROC of 0.99. Dataset 2 is used to test the model generalizability, achieving a classification accuracy of 97.0%. Conclusion: The model integrates well into primary care settings, as it relies on structured clinical data rather than imaging. Its low costs relative to value and high scalability make it suitable for population-level screening and treatment of osteoporosis. The limitation of the research is that we utilize only a clinical dataset to train the model without image analysis.

• Details
• Contents