Detection of Low Back Pain Based on Image Processing with Neural Network
Keywords:
Low back pain, remora optimized deep neural network (RODNN), MRI image, lumbar regionAbstract
Low back pain is a common ailment affecting a significant portion of the global population. Accurate and timely detection of the causes and severity of low back pain is crucial for effective treatment and management. Traditional diagnosis methods for low back pain often rely on subjective assessments and are prone to inter-observer variability. In recent years, image processing techniques have emerged as valuable tools for analyzing medical images and providing objective diagnostic information. In this study, we propose a novel remora-optimized deep neural network (RODNN) approach for detecting low back pain. Initially, we gathered an MRI dataset of the lumbar region to evaluate the effectiveness of the proposed method. These images are pre-processed using a Gaussian filter for noise reduction. Followed by, grey level co-occurrence matrix (GLCM) is used to extract the relevant features. The experimental results are analyzed regarding the accuracy, precision, recall, and f1-score metrics. Practical outcomes demonstrate the superior performance of the proposed RO-DNN technique in low back pain detection when compared to existing approaches.
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