Detection of bone fracture in upper extremities using visual geometric group-19 and compare the accuracy with CNN

The aim of this research paper is to compare the accuracy of a novel Visual Geometry Group-19 deep learning model and a CNN in order to detect bone fractures in the upper extremity regions of the hands. The two groups involved in this investigation are the Novel Visual Geometry Group-19 deep learning model and the CNN. This research makes use of an open source dataset titled "Bone Fracture Detection Using X-Rays" obtained from Kaggle. The sample size of the dataset consists of 9463 x-ray images. Test and train sets were constructed from the dataset. The training dataset comprises 8987 images, while the test dataset comprises 633 images. Deliberate consideration was given to the iteration sample size with a 95% confidence interval and a G power of 80%. Ten iterations of the experiment were conducted utilizing the aforementioned models. The analysis of the bone fracture dataset utilizing x-rays revealed that the convolutional neural network (CNN)achieved a 72.50% accuracy rate, while the innovative Visual Geometry Group-19 deep learning model achieved a 97.01% accuracy rate. The t-test indicates that the novel Visual Geometry Group-19 deep learning model exhibited greater statistical significance than the CNN (CNN), as evidenced by its p