A Novel Approach to Bone Fracture Detection: Analyzing the Potential of ReXNet150

Abstract

This paper presents a novel lightweight architecture known as ReXNet-150 for detecting fractures in bones in X-ray images. We show how fracture classification can be enhanced with the help of deep learning and how it can be helpful in healthcare. Our methodology includes the deep learning architecture ReXNet-150. It was informed through a particular dataset of X-rays, including 600 images of validation and 8863 images of training, including fractured and non-fractured images. Techniques such as scaling and normalization form part of data preprocessing to improve model performance. Also, the paper examines transfer learning methods of extracting features with pre-trained models. We evaluate the model effectiveness with the help of such metrics as accuracy, F1 score, and loss functions. Our proposed ReXNet-150 architecture has obtained the top testing accuracy of 92.2 percent and training accuracy of 99.8 percent, which illustrates the effectiveness of our model in a realtime use-case scenario. We consent to the shortcomings of our model, including the necessity to study the assortment of data further so as to enhance generalization. Everything being taken into consideration, our results demonstrate the possible use of ReXNet-150 as a reliable device in categorizing bone fractures, which will subsequently result in advancements in healthcare and medical diagnostics.

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