Bone-Net: a parallel deep convolutional neural network-based bone fracture recognition

Abstract

Many people suffer from bone fractures, which can result from minor accidents, forceful blows, or even diseases like osteoporosis or bone cancer. In the medical realm, accurately identifying bone fractures from X-ray images is paramount for effective diagnosis and treatment. To address this, a comparative study is conducted utilizing three distinct models: a traditional convolutional neural network (CNN), MobileNet-V2, and a newly developed parallel deep convolutional neural network (PDCNN). The primary aim is to evaluate and contrast these models in terms of precision, sensitivity, and specificity for diagnosing bone fractures. X-ray images of fractured and non-fractured bones are sourced from Kaggle and subjected to various image processing techniques to rectify anomalies. Techniques such as cropping, resizing, contrast enhancement, filtering, and augmentation are applied, culminating in canny edge detection. These processed images are then used to train and test models. The results showcased the superior performance of the newly developed PDCNN model, achieving an impressive accuracy of 92.89%, surpassing both the traditional CNN and pretrained MobileNet-V2 models. A series of ablation studies are conducted to fine-tune the hyperparameters of the PDCNN model, further validating its efficacy. Throughout the investigation, PDCNN consistently outperformed MobileNet-V2 and traditional CNN, underscoring its potential as an advanced tool for streamlining bone fracture identification.