Bone age assessment based on deep convolution neural network incorporated with segmentation

Gao, Yunhe; Zhu, Tao; Xu, Xiaoli

doi:10.1007/s11548-020-02266-0

Cited by 31 publications

(21 citation statements)

References 30 publications

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“…Gao et al utilized U-Net to acquire mask images of hand bone, and VGGNet was used to perform image classification tasks. 48 The background noise was removed with hand bone segmentation. Of note, the attention module was inserted into the VGGNet to focus on targeted regions and achieved an MAE of 9.997 months.…”

Section: The Prediction Of Bone Agementioning

confidence: 99%

Machine learning in orthodontics: Challenges and perspectives

Liu¹,

Chen²,

Li³

et al. 2021

Adv Clin Exp Med

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Section: The Prediction Of Bone Agementioning

confidence: 99%

Machine learning in orthodontics: Challenges and perspectives

Liu¹,

Chen²,

Li³

et al. 2021

Adv Clin Exp Med

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“…After learning, rather than manually extracting the feature, the matrixes serve as filters that slide over the main input image, and the convolution operation is carried out via equation (1). Finally, after training and mapping the input images to the output labels, several convolution layers extract the features [16].…”

Section: Deep Convolutionmentioning

confidence: 99%

COVID‐19 Detection Using Deep Convolutional Neural Networks and Binary Differential Algorithm‐Based Feature Selection from X‐Ray Images

2021

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The new COVID-19 is rapidly spreading and has already claimed the lives of numerous people. The virus is highly destructive to the human lungs, and early detection is critical. As a result, this paper presents a hybrid approach based on deep convolutional neural networks that are very effective tools for image classification. The feature vectors were extracted from the images using a deep convolutional neural network, and the binary differential metaheuristic algorithm was used to select the most valuable features. The SVM classifier was then given these optimized features. For the study, a database containing images from three categories, including COVID-19, pneumonia, and a healthy category, included 1092 X-ray samples, was used. The proposed method achieved a 99.43% accuracy, a 99.16% sensitivity, and a 99.57% specificity. Our findings indicate that the proposed method outperformed recent studies on COVID-19 detection using X-ray images.

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“…In recent years, deep learning methods have been successfully used in musculoskeletal imaging for lesion identification and severity assessment, such as a fracture (27)(28)(29)(30)(31)(32)(33)(34)(35)(36), knee lesion (37)(38)(39)(40)(41)(42), osteoarthritis (43)(44)(45)(46)(47), and spinal degenerative lesion (48,49). In addition, some models based on deep learning are adopted to assess bone age (50)(51)(52)(53)(54)(55) and determine sex (56) on radiographs. Therefore, it is plausible to use deep learning methods to establish diagnostic models for bone tumors as well, which may greatly reduce the misdiagnosis and missed diagnosis rates of bone tumors.…”

Section: Introductionmentioning

confidence: 99%

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

et al. 2022

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Deep learning is a subfield of state-of-the-art artificial intelligence (AI) technology, and multiple deep learning-based AI models have been applied to musculoskeletal diseases. Deep learning has shown the capability to assist clinical diagnosis and prognosis prediction in a spectrum of musculoskeletal disorders, including fracture detection, cartilage and spinal lesions identification, and osteoarthritis severity assessment. Meanwhile, deep learning has also been extensively explored in diverse tumors such as prostate, breast, and lung cancers. Recently, the application of deep learning emerges in bone tumors. A growing number of deep learning models have demonstrated good performance in detection, segmentation, classification, volume calculation, grading, and assessment of tumor necrosis rate in primary and metastatic bone tumors based on both radiological (such as X-ray, CT, MRI, SPECT) and pathological images, implicating a potential for diagnosis assistance and prognosis prediction of deep learning in bone tumors. In this review, we first summarized the workflows of deep learning methods in medical images and the current applications of deep learning-based AI for diagnosis and prognosis prediction in bone tumors. Moreover, the current challenges in the implementation of the deep learning method and future perspectives in this field were extensively discussed.

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Bone age assessment based on deep convolution neural network incorporated with segmentation

Cited by 31 publications

References 30 publications

Machine learning in orthodontics: Challenges and perspectives

Machine learning in orthodontics: Challenges and perspectives

COVID‐19 Detection Using Deep Convolutional Neural Networks and Binary Differential Algorithm‐Based Feature Selection from X‐Ray Images

Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges

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