Deep Learning Models for Accurate Diagnosis and Detection of Bone Pathologies: A Comprehensive Analysis and Research Challenges
DOI:
https://doi.org/10.52756/ijerr.2024.v40spl.009Keywords:
Artificial intelligence, bone disorder, convolutional neural network, learning models, medical imagingAbstract
The presence of bone disease has been observed to have a substantial influence on an individual's overall health. There are conventional techniques to detect as well as diagnose them but they often suffer limitations in the form of misdiagnosis because of manual error as well as maximum time consumption. Therefore, it is of utmost importance to accurately and proficiently identify it by integrating conventional methods with advanced artificial intelligence techniques. The objective of this study is to conduct a comprehensive analysis of the present state of research concerning the identification and diagnosis of bone disease using machine learning and deep learning. A review is conducted in accordance with the PRISMA guidelines which focus on the examination of scholarly articles published within the timeframe of 2019 to 2024. This review analyzes peer-reviewed literature and research findings to show how machine and deep learning can improve bone disease diagnosis accuracy. It has been found that in the case of osteoporosis, the highest recall, precision, and F1 score is computed by random forest with 93%, 94%, and 93%, respectively while as advanced CNN technique computed 98% accuracy for osteoporosis and 98.4% accuracy, 95% sensitivity as well as 97% specificity for osteonecrosis. Likewise, for bone tumor and osteoarthritis, AlexNet achieved 98% and 98.90% accuracy, respectively. The study introduces a novel approach to the diagnosis of bone diseases by emphasizing the usage of advanced learning techniques over conventional methods. Additionally, the paper highlights the significance of analyzing the clinical or imaging data and extracting features to improve image quality and provide a pathway toward more accurate and efficient diagnosis of bone diseases. By delving into these techniques, the paper offers valuable insights into enhancing diagnostic capabilities for bone diseases, which ultimately leads to improved patient care and treatment outcomes.
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