X-ray image analysis for osteoporosis diagnosis: From shallow to deep analysis

Mebarkia, Meriem; Meraoumia, Abdallah; Houam, Lotfi; Khemaissia, Seddik

doi:10.1016/j.displa.2022.102343

Cited by 10 publications

(2 citation statements)

References 53 publications

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“…Therefore, the diagnosis of COVID-19 can be carried out by chest X-ray images with the support of artificial intelligence technology. In recent years, deep learning methods represented by convolutional neural networks (CNN) have been widely used in medical image processing and have made important progress in many fields [6] , [7] , such as skin cancer [8] , [9] , breast cancer [10] , brain disease [11] , pneumonia [12] , [13] , and lung segmentation [14] , [15] , [16] , [17] , [18] . At present, many effective algorithms based on deep learning have been proposed, most of which are based on supervised learning strategies that require a large number of correctly labeled training samples to train a well-performing model.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 chest X-ray image classification in the presence of noisy labels

2023

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Section: Introductionmentioning

confidence: 99%

COVID-19 chest X-ray image classification in the presence of noisy labels

2023

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“…[34] The deep learning model developed in our research represents a potential tool for osteoporosis screening, utilizing the new advancements in Medical Sector. Exceptional performance has been demonstrated by Convolutional Neural Networks (CNNs) in medical image classification, with models like VGG16, VGG19, DenseNet121, InceptionV3, and Resnet50 [22,26,39] being among the most effective. However, due to the limited data availability of annotated medical images, training CNNs from scratch is often impractical.…”

Section: Introductionmentioning

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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