Real-time Classification of Bone Fractures Utilizing Different Convolutional Neural Network Approaches

Abstract

Fractures are a prevalent occurrence in the human anatomy, necessitating accurate and expeditious diagnosis in order to administer the correct medical treatment. Detecting fractures manually using X-ray pictures is time-consuming and risks human mistakes. This study proposes a novel fracture detection method utilizing AI-assisted approaches, notably deep learning, to overcome these concerns. A deep neural network (DNN) algorithm is used to get real-time fractured bones detection and classification. A limited dataset necessitates the application of data augmentation techniques to improve model performance and prevent overfitting. Using softmax activation and Adam optimizer, three experiments are conducted to determine the efficiency of the model. Using 5-fold cross-validation, the proposed model distinguishes between healthy and fractured bones with an impressive accuracy of 92.44 percent. Furthermore, the accuracy exceeds 95 percent and 93 percent when evaluated on 10 percent and 20 percent of the data, respectively. These outcomes demonstrate that the devised model is superior to existing methods. Utilizing the potential of artificial intelligence and deep learning, this research paves the way for accurate and efficient fracture diagnosis. This study presents a novel AI-assisted technique that could change fracture diagnosis by allowing for the immediate time inspection and classification of bone fractures.

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