Framework for Classifying Long Bone Detection Using Image Processing Techniques
Keywords:
Long Bone Detection, Image Processing TechniquesAbstract
Humans frequently have bone fractures, which can happen as a result of trauma to the bone, simple accidents, osteoarthritis, and bone cancer. Consequently, a critical component of the healthcare profession is the precise detection of bone fractures. In this research, bone fracture analysis is performed using X-ray pictures. The goal of this work is to create a fast and precise method for categorizing bone fractures based on the data obtained from x-ray pictures using image processing. The patients receive pictures of the broken bone, and processing systems such pre-processing, segmentation, edge detection, and feature extraction are used. The final classification of the analyzed pictures into fracture and nonfractured bone will evaluate the precision of various approaches. Our study demonstrates that the suggested strategy is straightforward and effective, making it desirable for ductile fracture identification and classification at a time when doctors and radiologists are interacting with an increasing number of patients and trying to reduce workload. In addition, this method outperforms state-of-the-art methods in positions of outcomes, run response period, and detecting accuracy.
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