NEXT-GENERATION BONE FRACTURE MODELING: TOWARD MORE ACCURATE AND REALISTIC PREDICTIONS
Akash Kumar Anmol Tripathi
Computers have proven indispensable in numerous aspects of human life, including banking, online shopping, communication, education, research, and medicine. To enhance patient care, numerous innovative technological resources have been developed for doctors and hospitals. One significant advancement addresses the limitations of conventional X-ray scanners, which often produce unclear images of bones, potentially leading to misdiagnoses of fractures by surgeons. The process involves several stages—pre-processing, edge detection, feature extraction, and machine learning classifications—all aimed at simplifying surgeons' tasks. Machine learning algorithms have become crucial in various fields, such as seismology, remote sensing, and medicine, with this program being a prime example. Specifically, algorithms like Naïve Bayes, Decision Tree, Nearest Neighbors, Random Forest, and SVM have been employed to detect bone fractures using a dataset of 270 X-ray images. The study reported accuracy rates for these algorithms ranging from 0.64 to 0.92, with SVM achieving the highest accuracy. Notably, SVM's performance surpassed that of most other reviewed studies.
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