Automated Hand X-Ray Based Gender Classification and Bone Age Assessment Using Convolutional Neural Network

Marouf, Muhammad; Siddiqi, Raheel; Bashir, Fatima; Vohra, Bilal

doi:10.1109/icomet48670.2020.9073878

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…Data analysis using the DenseNet-121 model pre-overtrained in the ImageNet data range gave an accuracy of 99% for cervical images and 98% for lumbar images. In [10] Marouf et al proposed a hybrid methodology for gender classification and bone age estimation, using the trained VGG-16 model and the RSNA dataset. They achieved an accuracy of 99% for the gender classification and for age classification they achieved an MAD 0.50 years and an RMS of 0.67 years.…”

Section: Introductionmentioning

confidence: 99%

Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

et al. 2022

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Age prediction from X-rays is an interesting research topic important for clinical applications such as biological maturity assessment. It is also useful in many other practical applications, including sports or forensic investigations for age verification purposes. Research on these issues is usually carried out using high-resolution X-ray scans of parts of the body, such as images of the hands or images of the chest. In this study, we used low-resolution, dual-energy, full-body X-ray absorptiometry images to train deep learning models to predict age. In particular, we proposed a preprocessing framework and adapted many partially pretrained convolutional neural network (CNN) models to predict the age of children and young adults. We used a new dataset of 910 multispectral images that were weakly annotated by specialists. The experimental results showed that the proposed preprocessing techniques and the adapted approach to the CNN model achieved a discrepancy between chronological age and predicted age of around 15.56 months for low-resolution whole-body X-rays. Furthermore, we found that the main factor that influenced age prediction scores was spatial features, not multispectral features.

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Section: Introductionmentioning

confidence: 99%

Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

et al. 2022

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“…CNN requires a huge amount of data to train itself which can then be used for supervised or unsupervised decision making. It does so by extracting features from the input data and adjusting weights of the neurons by forward and back-propagation [35]. There are many different CNN architectures available, trained on the ImageNet dataset and their weights can be used as initial weights for any classification problem.…”

Section: A Convolutional Neural Networkmentioning

confidence: 99%

“…For the first part, i.e., comparison of CI techniques, we select a pre-trained VGG-16 model (with ImageNet weight) as a fixed-feature extractor based on its structural simplicity [35], and Linear SVM as a classifier.…”

Section: B Experimental Setupmentioning

confidence: 99%

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A Hybrid VDV Model for Automatic Diagnosis of Pneumothorax Using Class-Imbalanced Chest X-Rays Dataset

et al. 2022

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Pneumothorax, a life-threatening disease, needs to be diagnosed immediately and efficiently. The prognosis, in this case, is not only time-consuming but also prone to human errors. So, an automatic way of accurate diagnosis using chest X-rays is the utmost requirement. To date, most of the available medical image datasets have a class-imbalance (CI) issue. The main theme of this study is to solve this problem along with proposing an automated way of detecting pneumothorax. To find the optimal approach for CI problem, we first compare the existing approaches and find that under-bagging method (referred as data-levelensemble formed by creating subsets of majority class and then combining each subset with all samples of minority class) outperforms other existing approaches. After selection of best approach for CI problem, we propose a novel framework, named as VDV model, for pneumothorax detection from highly imbalance dataset. The proposed VDV model is a complex model-level ensemble of data-level-ensembles and uses three convolutional neural networks (CNN) including VGG16, VGG-19, and DenseNet-121 as fixed feature extractors. In each data-level-ensemble, features extracted from one of the pre-defined CNN architectures are fed to support vector machine (SVM) classifier, and output is calculated using the voting method. Once outputs from the three data-level-ensembles (corresponding to three different CNN architectures as feature extractor) are obtained, then, again, the voting method is used to calculate the final prediction. Our proposed framework is tested on the SIIM ACR Pneumothorax dataset and Random Sample of NIH Chest X-ray dataset (RS-NIH). For the first dataset, 85.17% Recall with 86.0% Area under the Receiver Operating Characteristic curve (AUC) is attained. For the second dataset, 90.9% Recall with 95.0% AUC is achieved with a random split of data while 85.45% recall with 77.06% AUC is obtained with a patient-wise split of data. The comparison of our results for both the datasets with related work proves the effectiveness of proposed VDV model for pneumothorax detection.

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“…Albeit a small test dataset, their method has returned a relatively good prediction on the bone age just by relying on a single index finger. Similarly, the work by Marouf et al [ 45 ] has also implemented CNNs to provide an end-to-end system to predict the bone age. They have utilized the powerfulness of CNNs to predict bone age with the help of gender information with a mean absolute difference (MAD) of 0.5 years, where the gender classification used is less accurate with just 79.60% classification accuracy.…”

Section: Related Workmentioning

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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Automated Hand X-Ray Based Gender Classification and Bone Age Assessment Using Convolutional Neural Network

Cited by 16 publications

References 15 publications

Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

Age Prediction from Low Resolution, Dual-Energy X-ray Images Using Convolutional Neural Networks

A Hybrid VDV Model for Automatic Diagnosis of Pneumothorax Using Class-Imbalanced Chest X-Rays Dataset

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

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