Analysis and Classification of Bone Fractures Using Machine Learning Techniques

Abstract

Human bones are the hard organs that protect vital organs such as the heart, lungs, and other internal organs. Fractures of the bones are a prevalent issue among humans. Bone fractures may develop from an accident or another circumstance when there is great pressure on the bones. It may be difficult and time-consuming to determine the site of a fracture in a patient who is suffering discomfort. The manual examination of fractures during radiological interpretation is a time-consuming and error-prone process. This may result in erroneous detection, poor fracture healing, and an extensive procedure. So, this research proposed an effective approach to rectifying bone fractures with the inclusion of the latest technologies. The solution is proposed by employing a Deep learning model. Moreover, a novel concept of classification is also incorporated. Firstly; the MURA dataset was collected from Stanford. Secondly; The proposed model used techniques like DCNN (Deep Convolution Neural Network) and use Alex Net model. Bones are classified into fractured or non-fractured through a classification approach. The proposed model was created using Google Colab. The proposed model was trained by repeating several experiments. The performance was evaluated based on accuracy. The suggested model results were compared with baseline algorithms as well. Consequently, the findings of this work will be useful for the medical industry.

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