Efficient and early detection of osteoporosis using trabecular region

Vishnu, T; Saranya, K.; Arunkumar, R.; Devi, Manu

doi:10.1109/get.2015.7453840

Cited by 14 publications

(4 citation statements)

References 6 publications

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“…Fully connected layers are typically placed after the stacked convolutional and pooling layers in a CNN, and before the output, layer to carry out reasoning tasks. [37] In the domain of deep learning, transfer learning encompasses two primary types: Feature extraction and fine-tuning are both techniques commonly used in deep learning. [48] During the feature extraction standard dataset, ImageNet was used to remove the top classification layer.…”

Section: Transfer Learning Approachmentioning

confidence: 99%

Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs

Dodamani¹,

Danti²

2023

Int. J. Onl. Eng.

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Osteoporosis is a condition that affects the entire skeletal system, resulting in decreased density of bone mass and the weakening of bone tissue's micro-architecture. This leads to weaker bones that are more susceptible to fractures. Detecting and measuring bone mineral density has always been a critical area of focus for researchers in the diagnosis of bone diseases such as osteoporosis. However, existing algorithms used for osteoporosis diagnosis encounter challenges in obtaining accurate results due to X-ray image noise and variations in bone shapes, especially in low contrast conditions. Therefore, the development of efficient algorithms that can mitigate these challenges and improve the accuracy of osteoporosis diagnosis is essential. In this research paper, a comparative analysis was conducted Assessing the accuracy and efficiency of the latest deep learning CNN model, such as VGG16, VGG19, DenseNet121, Resnet50 and InceptionV3 in detecting to Classify Normal and Osteoporosis cases. The study employed 830 X-ray images of Spine, Hand, Leg, Knee, and Hip, comprising of Normal (420) and Osteoporosis (410) cases. Various performance metrics were utilized to evaluate each model, and the findings indicate that DenseNet121 exhibited superior performance with an accuracy rate of 93.4% with Achieving an error rate of 0.07 and a validation loss of only 0.57 in comparison with other models considered in this study.

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Section: Transfer Learning Approachmentioning

confidence: 99%

Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs

Dodamani¹,

Danti²

2023

Int. J. Onl. Eng.

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“…The third metacarpal bone and distal radius were automatically located and segmented using a region of interest (ROI) that Areeckal et al [21] introduced to their automatic segmentation technique. Then, they employed cortical radiogrammetry, a low-cost pre-screening technology, to find people with low bone mass.…”

Section: Texture Analysis For Osteoporosis Detectionmentioning

confidence: 99%

“…The gradient values of the central pixel are then contrasted with the gradient values of the predefined threshold. There are two sets in the convolution template, one at the level edge of detection and the other for the vertical edge of detection [21,[33][34][35]. Since it operates on the gradient expression and the f(x,y) twodimensional image function, it is presented as follows:…”

Section: Sobel Edge Detectionmentioning

confidence: 99%

Non-Invasive Machine Learning-Based Classification of Bone Health

Bhise¹,

Havaldar²

2022

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Osteoporosis is a disease that affects both men and women of all ages but is more commonly seen in women. A measure called Bone Mineral Density (BMD) is often used to raise a warning about the disease. BMD is calculated using a variety of image processing algorithms in both X-ray and dual energy X-ray absorptiometry (DEXA) images. It is a measure of the important T-score, which reflects the degree of osteoporosis. There are many ways to quantify BMD, but DEXA is often regarded as the gold standard. The significance of DEXA images for osteoporosis detection was found in several research. The healthcare system has a serious issue with the lack of osteoporosis education and screening. There is a ton of literature available for diagnosing osteoporosis as well. The numerous methods for detecting osteoporosis will be covered in this review. The problems from the literature analysis, image processing algorithms for detecting osteoporosis, interpretations of the results, and potential recommendations are all included in this work.

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“…The first possibility to record images is radiography. Vishnu et al [9] and Singh et al [10] created classification networks from X-ray images. Singh et al [10] additionally compared the results to other machine learning algorithms like support vector machines.…”

Section: Introductionmentioning

confidence: 99%

Inverse modeling of cancellous bone using artificial neural networks

2022

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Artificial neural networks are used to solve different tasks of daily life, engineering and medicine. In this work, we investigate its suitability for the examination of simulation results of cancellous bone with the aim to evaluate whether the bone is affected by osteoporosis. This bone disease is characterized by a reduction of the cortical bone phase, one of the two main components of the bone. The neural network predicts the simulated volume fraction in different parts of a cylinder, which models the bone. As a basis for its calculations, the neural network gets the information about the magnetic field inside the cylinder from finite element simulations. Examinations show that it is possible to train neural networks on solving that task with very high accuracies.

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Efficient and early detection of osteoporosis using trabecular region

Cited by 14 publications

References 6 publications

Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs

Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs

Non-Invasive Machine Learning-Based Classification of Bone Health

Inverse modeling of cancellous bone using artificial neural networks

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