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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Classification of Osteoporosis in the Lumbar Vertebrae using L2 Regularized Neural Network based on PHOG Features

Author 1: Kavita Avinash Patil
Author 2: K. V. Mahendra Prashanth
Author 3: A Ramalingaiah

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130449

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 4, 2022.

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Abstract: One of the most common bone diseases in humans is osteoporosis, which is a major concern for the public health. Osteoporosis can be prevented if it is detected at an early stage. The research agenda consists of two phases: pre-processing of X-ray images of the spine and analysis of texture features from trabecular bone lumbar vertebrae L1-L4 for detecting osteoporosis. The preprocessing involves image enhancement of texture features and co-register the images in order to segment the L1-L4 regions in the lumbar spine. Range filtering and Pyramid Histogram of Orientation Gradient (PHOG) are used to analyze texture features. Input images are filtered with a range filter to adjust the local sub range intensities in a specified window to detect edges. Then a PHOG algorithm is designed to determine both the local shape of an image texture and its spatial layout. Based on texture features of lumbar vertebrae L1-L4, classify them as normal or osteoporotic using neural network (NN) models with L2 regularization. In an experiment, X-ray images and dual-energy X-ray absorptiometry (DXA) reports of individual patients are used to verify the system. DXA reports describe a statistical analysis of normal and osteoporotic results. However, the proposed work is categorized according to the texture features as normal or osteoporotic. 99.34% classification accuracy is achieved; cross-validation of these classified results is done with the DXA reports. Diagnostic accuracy of the proposed method is higher than that of the existing DXA with X-ray. Further, the area under the Receiver Operating Characteristic (ROC) curve for L1-L4 had a significantly higher sensitivity for osteoporosis.

Keywords: Cross validation; image enhancement; lumbar spine; lumbar vertebrae; neural network; osteoporosis; PHOG; regularization; trabecular bone; texture features; x-ray images

Kavita Avinash Patil, K. V. Mahendra Prashanth and A Ramalingaiah, “Classification of Osteoporosis in the Lumbar Vertebrae using L2 Regularized Neural Network based on PHOG Features” International Journal of Advanced Computer Science and Applications(IJACSA), 13(4), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130449

@article{Patil2022,
title = {Classification of Osteoporosis in the Lumbar Vertebrae using L2 Regularized Neural Network based on PHOG Features},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130449},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130449},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {4},
author = {Kavita Avinash Patil and K. V. Mahendra Prashanth and A Ramalingaiah}
}


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