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Bone fractures are common in diabetic patients and can result in several musculoskeletal conditions. Both type 1 and type 2 diabetes substantially increase the risk and severity of bone fractures. Prompt treatment and management of diabetes and its complications are crucial to mitigate this serious complication. Detection and diagnosis in its early stage can reduce the challenging conditions in treatment. Traditional image processing techniques like digital-geometric analysis, entropy measures, and gray-level co-occurrence matrices have been used for automated bone fracture detection. However, these detection methods rely neither on healthy controls nor diabetic-affected patients. Only few studies focused on detecting fractures in diabetic patients. The rising prevalence of diabetic ankle fractures made the study emphasize the development of a fracture detection model based on the Meta Magnify (MetaMag) efficiency model. The proposed model involves the Lower Extremity Radiographs (LERA) dataset, which consists of image samples of normal and abnormal lower extremities of the body, such as the hip, ankle, knee, and foot. Pre-processing involves a one-hot encoding method that handles the missing data and represents categorical variables as numerical values. Further, the classification is performed using the MetaMag efficiency model, incorporated with MetaMag scaling and unified normalization. Further, the efficiency of the proposed model is analyzed by comparing it with conventional EfficientNet and another model. Finally, the proposed work's performance is analyzed using evaluation measures such as accuracy, precision, recall and F1-score. The results indicate the improved efficiency of the model.
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