Evaluation of different Deep Learning models in Classification of Fractured and Non-Fractured X-ray Images

Evaluation of Different Deep Learning Models in the Classification of Fractured and Non-Fractured X-ray Images Student's Name Institution Course # and Name Professor's Name Submission Date Evaluation of Different Deep Learning Models in the Classification of Fractured and Non-Fractured X-ray Images 1 Introduction Bones are often viewed as immobile structures that provide physical reinforcement. The process of bone remodeling continues throughout an individual's life, governed primarily by physiological requirements. According to Cowan and Kahai (2020), newborns typically have 270 bones, which fuse to form roughly 206 bones in adulthood. These include the skull bones, vertebrae, rib cage, and upper and lower extremities. Anatomical changes in specific bones lead to the variety in their number. An organism's skeletal structure comprises calcium-rich connective tissue and bone-specific cells. Fractures can be caused by pressure on a bone or by certain conditions (Mohanty & Senapati, 2023). The conventional approach to diagnosing fractures mostly depends on radiologists' ability to visually study X-ray images to identify and categorize fractures (Sharma, 2023). Every year, a substantial number of people suffer fractures, necessitating a prompt and correct diagnosis to avoid long-term injury or death. X-rays are widely utilized in diagnosing bone fractures due to their rapidity, affordability, and user-friendliness, making them one of the primary tools in medical imaging. Medical imaging is essential for diagnosing and treating various medical disorders, such as fractures in orthopedics. AI has rapidly attracted more attention as a means of improving medical imaging interpretation and raising diagnosis accuracy. Accurate fracture detection is essential for determining the most appropriate treatment and forecasting the result. Fracture detection and classification have extensively utilized traditional machine learning methods for pre-processing, feature extraction, and classification. Aso-Escario et al. (2019) identified the delayed detection of spine fractures as a significant public health hazard. Moreover, if a fracture is misdiagnosed or undiagnosed, it can lead to nonunion, malunion, or additional harm to the surrounding tissues and the fractured bone. The traditional fracture detection comprises radiologists visually examining X-rays to identify and categorize fractures. However, many factors can make X-ray interpretation difficult. However, According to Sharma (2023), this approach has the potential to be time-consuming, based on personal judgment, and susceptible to mistakes, especially with complex fracture patterns or slight anomalies. Radiologists are often exhausted and make mistakes due to excessive workloads and tight deadlines. Patients, doctors, and radiologists can be harmed by incorrect fracture diagnosis. The study by Taylor-Phillips and Stinton (2019) indicated that radiologists have poor focus, eye fatigue, and fracture detection. This shows how weariness affects diagnosis accuracy. Emergency misdiagnosis can increase without a second opinion. Medical imaging applications show deep learning (DL) efficacy. CNNs and RNNs, deep learning models, can represent imaging data hierarchically and independently recognize fracture patterns. CNN-based deep learning algorithms excel at picture recognition. This makes them ideal for medical picture interpretation. Sharma (2023) suggests training these models on large datasets with annotations to improve performance and fracture detection sensitivity and specificity. This helps identify subtle patterns and features humans may overlook in X-ray pictures, improving fracture detection accuracy and efficiency. In this context, several deep learning models will be evaluated for the classification of X-ray images into fractured and non-fractured. The aim is to improve comprehension of the theme and come up with suggestions that future researchers can use. Objective and Scope of the Study The purpose of this research is to examine the performance of different DL models on X-ray images that are either fractured or non-fractured. The effectiveness of three widely used CNN architectures, ResNet50, VGG16, and Inception, will be assessed in this study. The selection of these models is based on their capability to perform different image classification tasks, including medical imaging. Research objective: The primary aim of this pilot s...