Multi Region-Based Feature Connected Layer (RB-FCL) of deep learning models for bone age assessment

Wibisono, Ari; Mursanto, Petrus

doi:10.1186/s40537-020-00347-0

Cited by 17 publications

(9 citation statements)

References 47 publications

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“…For instance, input radiographs can be standardized and preprocessed, and bone age assessments can be performed. 34 Since CNNs can automatically recognize the hierarchy of discriminative features by training a set of labeled bone images, this technique can also identify various defects and porosities in the interlayer boundaries during printing, 35 which play a vital role in bone printing performance analysis.…”

Section: Bioprinting and Computer Visionmentioning

confidence: 99%

Computer vision-aided bioprinting for bone research

et al. 2022

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Bioprinting is an emerging additive manufacturing technology that has enormous potential in bone implantation and repair. The insufficient accuracy of the shape of bioprinted parts is a primary clinical barrier that prevents widespread utilization of bioprinting, especially for bone design with high-resolution requirements. During the last five years, the use of computer vision for process control has been widely practiced in the manufacturing field. Computer vision can improve the performance of bioprinting for bone research with respect to various aspects, including accuracy, resolution, and cell survival rate. Hence, computer vision plays a substantial role in addressing the current defect problem in bioprinting for bone research. In this review, recent advances in the application of computer vision in bioprinting for bone research are summarized and categorized into three groups based on different defect types: bone scaffold process control, deep learning, and cell viability models. The collection of printing parameters, data processing, and feedback of bioprinting information, which ultimately improves printing capabilities, are further discussed. We envision that computer vision may offer opportunities to accelerate bioprinting development and provide a new perception for bone research.

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Section: Bioprinting and Computer Visionmentioning

confidence: 99%

Computer vision-aided bioprinting for bone research

et al. 2022

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“…Moreover, they have defined five ROIs that cover carpal and phalanges regions based on the domain knowledge by the radiologists. Moreover, a similar ROIs division has been introduced in [ 40 ], in which they have argued that obtaining specific features will further improve the regressor performance in predicting the bone age. Then, each of these regions has been trained using three different deep learning models, namely DenseNet-121, Inception-V3, and InceptionResnet-V2, which will be the input to a Random Forest regressor.…”

Section: Related Workmentioning

confidence: 99%

“…Wibisono and Mursanto [40] DenseNet and InceptionResNet V2 -The hand X-ray images are divided into five different deep learning models are used to produce the feature maps.…”

Section: Model Strength Weaknessmentioning

confidence: 99%

Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

et al. 2021

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Skeletal bone age assessment using X-ray images is a standard clinical procedure to detect any anomaly in bone growth among kids and babies. The assessed bone age indicates the actual level of growth, whereby a large discrepancy between the assessed and chronological age might point to a growth disorder. Hence, skeletal bone age assessment is used to screen the possibility of growth abnormalities, genetic problems, and endocrine disorders. Usually, the manual screening is assessed through X-ray images of the non-dominant hand using the Greulich–Pyle (GP) or Tanner–Whitehouse (TW) approach. The GP uses a standard hand atlas, which will be the reference point to predict the bone age of a patient, while the TW uses a scoring mechanism to assess the bone age using several regions of interest information. However, both approaches are heavily dependent on individual domain knowledge and expertise, which is prone to high bias in inter and intra-observer results. Hence, an automated bone age assessment system, which is referred to as Attention-Xception Network (AXNet) is proposed to automatically predict the bone age accurately. The proposed AXNet consists of two parts, which are image normalization and bone age regression modules. The image normalization module will transform each X-ray image into a standardized form so that the regressor network can be trained using better input images. This module will first extract the hand region from the background, which is then rotated to an upright position using the angle calculated from the four key-points of interest. Then, the masked and rotated hand image will be aligned such that it will be positioned in the middle of the image. Both of the masked and rotated images will be obtained through existing state-of-the-art deep learning methods. The last module will then predict the bone age through the Attention-Xception network that incorporates multiple layers of spatial-attention mechanism to emphasize the important features for more accurate bone age prediction. From the experimental results, the proposed AXNet achieves the lowest mean absolute error and mean squared error of 7.699 months and 108.869 months2, respectively. Therefore, the proposed AXNet has demonstrated its potential for practical clinical use with an error of less than one year to assist the experts or radiologists in evaluating the bone age objectively.

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“…Further Long Short Term Memory (LSTM) was reported to be used in study of Yuan et al [29] for deep feature extraction. The work carried out by Wibisono and Mursanto [30] have also presented a sophisticated feature extraction scheme using deep learning. Hence, there exists various studies towards feature extraction in existing system.…”

Section: Related Studiesmentioning

confidence: 99%

A Novel Feature Extraction for Complementing Authentication in Hand-based Biometric

Mahalakshmi¹,

Sheela²

2021

IJACSA

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With an increasing usage of hand-based biometrics in authentication system, there is a need to evolve up with more potential security owing to increasing evolution of threats. The security of the hand authentication system completely depends upon uniqueness and distinct selection of features from hand image which has the properties of robustness, fault tolerance, and simpler implication. Review of existing feature extraction literatures shows more inclination towards sophisticated process as well as it also suffers from various other limitation. Therefore, this manuscript resolves this limitation by presenting a novel model of feature extraction which is carried out in more progressive form and less iterative form, unlike existing approaches. The proposed system achieves its research goal by introducing simplified feature extraction operation via storage, blurring, color space conversion, binary image conversion, the modelling aspect of the study emphasizes on image enhancement along with fuzzification for yielding more efficient result. An experimental study has been carried out using Python considering hand-biometric dataset in order to carry analysis where the outcome shows significant supportability over any palmprint recognition system. The study outcome is compared with most standard implementation of feature extraction to find that proposed system offer better accuracy performance in contrast with existing system.

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Multi Region-Based Feature Connected Layer (RB-FCL) of deep learning models for bone age assessment

Cited by 17 publications

References 47 publications

Computer vision-aided bioprinting for bone research

Computer vision-aided bioprinting for bone research

Intelligent Bone Age Assessment: An Automated System to Detect a Bone Growth Problem Using Convolutional Neural Networks with Attention Mechanism

A Novel Feature Extraction for Complementing Authentication in Hand-based Biometric

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