An Extensive Study of The Diagnosis and Classification of Fractures Using Machine Learning Deep Learning and Statistical Modelling Techniques

Abstract

Abstract newlineResearchers have taken a variety of approaches to bone fracture diagnosis, classification, and newlinecategorization. Nevertheless, there is not currently a uniform classification in place for any and newlineall of the fractures that have been found. The disciplines of machine learning and deep learning newlinehave seen a surge in attention recently. These two subfields are included in the category of newlineartificial intelligence. Deep Neural Networks, often known as DNNs, are well-known models newlinebecause of their capacity to classify images and their capacity to find solutions to challenging newlineproblems. The feature extraction methods SURF and SIFT were used by a variety of different newlinemachine learning and deep learning algorithms, including RF, KNN, SVM, Inception V3, and newlineResNeXt101, to detect and classify (normal, comminute, oblique, spiral, greenstick, impacted, newlineand transverse) bone fractures. The objective of this research is to create an image processing newlinesystem that is able to reliably and rapidly classify bone fractures by making use of data received newlinefrom X-rays. The X-ray images of the shattered bone that were obtained from the hospital need newlineto be processed, and this involves applying processing techniques such as pre-processing, newlinequality enhancement, and extraction. The images are then classified into fractured and newlineunfractured bones, and the precision of these classifications is compared to that of other newlinealgorithms such as K-Nearest Neighbor, Support Vector Machine, Random Forest, newlineInceptionV3, and ResNeXt101. newlineRecent years have seen a rise in interest in the use of deep learning strategies for the newlineclassification of images. Deep Neural Networks (DNN) have the ability to classify images and newlinesolve difficult issues. The purpose of this study was to develop, construct, and assess a deep newlinelearning system for the identification and classification of bone fractures (BFC). By utilizing newlineCT scans and X-rays, the goal of this effort is to develop an image-processing system that is newlinecapable of identifying bone fractures. The images are then div