Fractal gradient divergence-tuned deep belief network for osteoporosis detection using X-ray images.

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Abstract

Osteoporosis is a major illness that reduces bone strength, increasing the likelihood of fractures. Various imaging modalities are employed in the prediction and diagnosis of osteoporosis, including X-ray and Computed Tomography (CT). To assess the risk of fractures and bone disease, various machine learning (ML) techniques have been utilized. However, accurate diagnosis based on X-ray images remains a major concern for osteoporosis prediction. To improve the accuracy of osteoporosis disease prediction and reduce time, a novel Transformed Sampling Fractal Gradient Divergence Tuned Deep Belief Network (TSFGDTDBN) is proposed. It includes image acquisition, Augmentation, preprocessing, feature extraction, classification, and fine-tuning. Initially, numerous X-ray knee images were collected from the dataset during the acquisition phase. Rotation-transformed Image-SMOTE-based augmentation was employed for creating synthetic image samples of the minority class. The Gradient Divergence Tuned Deep Belief Network (DBN) involves two primary steps: layer-by-layer training and fine-tuning. In layer-by-layer training, preprocessing is performed with the Grubbs-normalized linear box blur method. Edge, shape, and texture features are extracted. Finally, the disease is classified as normal, Osteopenia, and Osteoporosis. During fine-tuning, hyperparameters are optimally adjusted using the Gradient Divergence Crow Search Tuning algorithm to improve classification accuracy. It enhances the deep neural network's performance and overall learning efficiency in osteoporosis disease prediction. Experimental evaluation is conducted using X-ray images with various factors. TSFGDTDBN improves accuracy with minimal time compared to conventional deep learning methods.

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