Osteoporosis diagnosis in knee X-rays by transfer learning based on convolution neural network

Wani, Insha Majeed; Arora, Sakshi

doi:10.1007/s11042-022-13911-y

Cited by 22 publications

(13 citation statements)

References 69 publications

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“…All included studies were published between 2018 and 2023, with most being published after 2020. X-ray (17/40) 14 – 25 and CT (16/40) 26 – 41 images were the most frequently used modalities; digital projection radiography (DPR) 42 – 44 was used in three studies and MRI 45 , 46 in two studies. Two studies 47 , 48 using vertebral fracture assessment (VFA) images were considered in this review.…”

Section: Resultsmentioning

confidence: 99%

“…Wani et al. 16 used transfer learning to develop four CNN networks: AlexNet, ResNet, VggNet-16, and VggNet-19. To mitigate the risk of overfitting, data augmentation techniques were applied, considering the limited amount of data available.…”

Section: Resultsmentioning

confidence: 99%

“…Transfer learning has gained popularity in the field of DL owing to its ability to leverage knowledge from previous tasks, improving generalization and achieving more accurate outcomes with limited data input. Wani et al 16 used transfer learning to develop four CNN networks: AlexNet, ResNet, VggNet-16, and VggNet-19. To mitigate the risk of overfitting, data augmentation techniques were applied, considering the limited amount of data available.…”

Section: Studies On Osteoporosis Screeningmentioning

confidence: 99%

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Deep learning in the radiologic diagnosis of osteoporosis: a literature review

He,

Lin,

Zhu

et al. 2024

J Int Med Res

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Objective Osteoporosis is a systemic bone disease characterized by low bone mass, damaged bone microstructure, increased bone fragility, and susceptibility to fractures. With the rapid development of artificial intelligence, a series of studies have reported deep learning applications in the screening and diagnosis of osteoporosis. The aim of this review was to summary the application of deep learning methods in the radiologic diagnosis of osteoporosis. Methods We conducted a two-step literature search using the PubMed and Web of Science databases. In this review, we focused on routine radiologic methods, such as X-ray, computed tomography, and magnetic resonance imaging, used to opportunistically screen for osteoporosis. Results A total of 40 studies were included in this review. These studies were divided into three categories: osteoporosis screening (n = 20), bone mineral density prediction (n = 13), and osteoporotic fracture risk prediction and detection (n = 7). Conclusions Deep learning has demonstrated a remarkable capacity for osteoporosis screening. However, clinical commercialization of a diagnostic model for osteoporosis remains a challenge.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Studies On Osteoporosis Screeningmentioning

confidence: 99%

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Deep learning in the radiologic diagnosis of osteoporosis: a literature review

He,

Lin,

Zhu

et al. 2024

J Int Med Res

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“…Looking ahead, the authors identified future avenues for research, including further optimization of their proposed modified U-Net architecture, exploration of additional attention mechanisms, and expansion of the methodology to encompass more diverse datasets for improved robustness and applicability. [2] Kumar, Goswami, and Batra (2023) aimed to devise a cost-effective diagnostic technology for the early detection of osteoporosis by categorizing knee X-ray images into normal, osteopenia, and osteoporosis categories. Their study utilized a dataset comprising X-rays from 240 subjects, with varying bone density conditions: 37 normal, 154 osteopenia, and 49 osteoporotic cases.…”

Section: Introductionmentioning

confidence: 99%

Osteoporosis Diagnosis through Visual Segmentation and Classification: Extensive Review

Sekhar,

Sharmila,

Narayana

et al. 2024

Int. J. Res. Publ. Rev.

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Osteoporosis, a prevalent and progressive bone disorder, is characterized by diminished bone density and quality, resulting in heightened fragility and susceptibility to fractures. Various factors such as age, gender, hormonal fluctuations, and genetics contribute to the development of osteoporosis. Given its widespread prevalence, accurate and timely diagnosis is essential, prompting the exploration of advanced diagnostic methodologies. This survey aims to review and analyze the utilization of advanced technologies in bone health assessment, with a focus on image segmentation and deep learning models. Specifically, Convolutional Neural Networks (CNNs) are employed for feature extraction, followed by the application of transfer learning algorithms such as VGG-19, U-Net, and RESNET. The integration of pre-trained models and transfer learning techniques enhances the models' capability to discern variations in osteoporosis severity. Evaluation metrics including F1score, recall, and confusion matrix are utilized to assess the performance of the reviewed approaches.

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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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Osteoporosis diagnosis in knee X-rays by transfer learning based on convolution neural network

Cited by 22 publications

References 69 publications

Deep learning in the radiologic diagnosis of osteoporosis: a literature review

Deep learning in the radiologic diagnosis of osteoporosis: a literature review

Osteoporosis Diagnosis through Visual Segmentation and Classification: Extensive Review

Transfer Learning-Based Osteoporosis Classification Using Simple Radiographs

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