Abstract <p>Osteoporosis (OP) is a prevalent metabolic bone disease characterized by decreased bone density and strength, leading to an increased risk of fractures, especially among the elderly. This condition often remains undiagnosed until a fracture occurs, making early detection crucial to prevent complications, reduce morbidity, and improve the patient’s quality of life. Currently the bone density was to identify the osteoporosis, and Dual Energy X-ray Absorptiometry (DEXA) for figuring out the size of the bone, time consuming, and the manual interpretation having the error. A novel fusion method for radiography images contrast augmentation was developed in order to avoid this issue. Initially, both X-Ray and subject records are considered as the dataset. The images are enhanced for clear bone identification through pre-processing techniques including resizing, anisotropic diffusion, contrast stretching, and high boost filtering. After pre-processing the images are segmented using the fuzzy C-means clustering segmentation. Features extraction is performed on the segmented images using the mean, skewness, kurtosis, gabour, grey level co-occurrence matrix, and discrete wavelet transform. The extracted images are merged with subject records using the canonical correlation analysis. The selected features are fed into the IDBNN to detect the condition of the osteoporosis prediction while the Dingo optimization algorithm by selecting the optimal weight. The proposed model has 94% accuracy and a 10% false positive rate. The suggested approach could detect osteoporosis with less computational time, making it ideal for real-time applications.</p>