International Journal on Science and Technology

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Bone Fracture Detection System Using Machine Learning

Author(s) GOTTIMUKKULA SRAVAN RAO, B.SHREYA, P.SWETHA SRI, Y.SUDARSHAN
Country India
Abstract Bone fractures are a common medical condition that can result from trauma, accidents, or certain diseases. Accurate and timely detection of fractures is crucial for effective treatment and recovery. Traditional methods of fracture detection primarily rely on manual interpretation of X-ray images by radiologists, which can be time-consuming and prone to human error. In recent years, advancements in machine learning and computer vision have paved the way for automated systems that can assist in the detection of bone fractures. This paper proposes a bone fracture detection system that utilizes deep learning techniques to automatically identify fractures in medical images, such as X-rays or CT scans. The proposed system aims to improve diagnostic accuracy, reduce detection time, and support medical professionals in clinical decision-making. The system's performance is evaluated against traditional methods, highlighting its potential to enhance the efficiency and reliability of fracture diagnosis. Leveraging deep learning techniques, particularly Conventional Neural Networks (CNN), the system enhances accuracy and reduces the time required for diagnosis. Traditional manual analysis by radiologists, while expert-driven, is time-consuming and prone to errors.
Keywords machine learning (ML) and deep learning (DL) algorithms
Field Engineering
Published In Volume 16, Issue 2, April-June 2025
Published On 2025-05-19
DOI https://doi.org/10.71097/IJSAT.v16.i2.5322
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