Research on Bone Age Automatic Judgment Algorithm Based on Deep Learning and Hand X-ray Image

2021 ◽  
Vol 11 (1) ◽  
pp. 156-161
Author(s):  
Xiang Shen ◽  
Feng Zhu ◽  
Zhi Sun ◽  
Shuli Zhao
Keyword(s):  
Feature Extraction ◽  
Evaluation Model ◽  
Image Data ◽  
Bone Age ◽  
Skeletal Age ◽  
Test Model ◽  
Automatic Assessment ◽  
Neural Network Learning ◽  
X Ray ◽  
Hand Bones

Objective: To learn the depth of paper using feature extraction, combined with critical areas of heat syndrome and related information, X-ray image of hand to analyze bone age children. Methods: The thesis of the X-ray image data preprocessing left hand, the use of depth of depth neural network learning methods, combined with clinical data skeletal age evaluation model to evaluate the effectiveness of the test model. Results: X-ray image of hand artificial feature extraction, combined SVM classification, automatic assessment of skeletal age. The method of automatic assessment of bone age SVM-based feature primarily artificial, SIFT features extracted image, LBP features, characteristics of GCLM, these features are combined, and then used to train the SVM, have some ability to automatically assess bone age assessment based on SVM. Conclusion: This topic X-ray image based on the hand bones, computer vision, machine learning to extract the relevant methods, pretreatment and segmentation of X-ray images of the hand bones, characterized by automatic assessment of bone age, lack the core image of the sample problem.

scholarly journals Image X-Ray Classification for COVID-19 Detection Using GCLM-ELM

2021 ◽  
Vol 7 (1) ◽  
pp. 74-85
Author(s):  
Vivin Umrotul M. Maksum ◽  
Dian C. Rini Novitasari ◽  
Abdulloh Hamid
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Extreme Learning Machine ◽  
Image Data ◽  
Activation Function ◽  
X Ray ◽  
Machine Learning Classification ◽  
Chest X Ray ◽  
Learning Machine ◽  
Hidden Layer

COVID-19 is a disease or virus that has recently spread worldwide. The disease has also taken many casualties because the virus is notoriously deadly. An examination can be carried out using a chest X-Ray because it costs cheaper compared to swab and PCR tests. The data used in this study was chest X-Ray image data. Chest X-Ray images can be identified using Computer-Aided Diagnosis by utilizing machine learning classification. The first step was the preprocessing stage and feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). The result of the feature extraction was then used at the classification stage. The classification process used was Extreme Learning Machine (ELM). Extreme Learning Machine (ELM) is one of the artificial neural networks with advanced feedforward which has one hidden layer called Single Hidden Layer Feedforward Neural Networks (SLFNs).  The results obtained by GLCM feature extraction and classification using ELM achieved the best accuracy of 91.21%, the sensitivity of 100%, and the specificity of 91% at 135° rotation using linear activation function with 15 hidden nodes.

Bone age determination using dual-energy X-ray absorptiometry

2017 ◽  
Author(s):  
Khalaf Alshamrani ◽  
Amaka Offiah ◽  
Elzene kruger
Keyword(s):  
Age Determination ◽  
Bone Age ◽  
Dual Energy ◽  
X Ray

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

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 765
Author(s):  
Mohd Asyraf Zulkifley ◽  
Nur Ayuni Mohamed ◽  
Siti Raihanah Abdani ◽  
Nor Azwan Mohamed Kamari ◽  
Asraf Mohamed Moubark ◽  
...  
Keyword(s):  
Bone Growth ◽  
Bone Age ◽  
Attention Mechanism ◽  
Assessment System ◽  
Dominant Hand ◽  
Age Assessment ◽  
Image Normalization ◽  
X Ray ◽  
Bone Age Assessment ◽  
Skeletal Bone

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.

scholarly journals Fuzzy enhancement and deep hash layer based neural network to detect Covid-19

2021 ◽  
pp. 1-11
Author(s):  
Amita Nandal ◽  
Marija Blagojevic ◽  
Danijela Milosevic ◽  
Arvind Dhaka ◽  
Lakshmi Narayan Mishra
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Performance Metrics ◽  
Regularization Parameter ◽  
Fuzzy Membership Function ◽  
Kappa Statistics ◽  
Pixel Intensity ◽  
Testing Sample ◽  
X Ray ◽  
Conventional Methods

This paper proposes a deep learning framework for Covid-19 detection by using chest X-ray images. The proposed method first enhances the image by using fuzzy logic which improvises the pixel intensity and suppresses background noise. This improvement enhances the X-ray image quality which is generally not performed in conventional methods. The pre-processing image enhancement is achieved by modeling the fuzzy membership function in terms of intensity and noise threshold. After this enhancement we use a block based method which divides the image into smooth and detailed regions which forms a feature set for feature extraction. After feature extraction we insert a hashing layer after fully connected layer in the neural network. This hash layer is advantageous in terms of improving the overall accuracy by computing the feature distances effectively. We have used a regularization parameter which minimizes the feature distance between similar samples and maximizes the feature distance between dissimilar samples. Finally, classification is done for detection of Covid-19 infection. The simulation results present a comparison of proposed model with existing methods in terms of some well-known performance indices. Various performance metrics have been analysed such as Overall Accuracy, F-measure, specificity, sensitivity and kappa statistics with values 93.53%, 93.23%, 92.74%, 92.02% and 88.70% respectively for 20:80 training to testing sample ratios; 93.84%, 93.53%, 93.04%, 92.33%, and 91.01% respectively for 50:50 training to testing sample ratios; 95.68%, 95.37%, 94.87%, 94.14%, and 90.74% respectively for 80:20 training to testing sample ratios have been obtained using proposed method and it is observed that the results using proposed method are promising as compared to the conventional methods.

scholarly journals COVID-19 diagnosis from chest X-ray images using transfer learning: Enhanced performance by debiasing dataloader

2021 ◽  
Vol 29 (1) ◽  
pp. 19-36
Author(s):  
Çağín Polat ◽  
Onur Karaman ◽  
Ceren Karaman ◽  
Güney Korkmaz ◽  
Mehmet Can Balcı ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Critical Time ◽  
Image Data ◽  
Mapping Method ◽  
Fine Tuning ◽  
Learning Stage ◽  
X Ray ◽  
Testing Dataset ◽  
X Ray Imaging ◽  
Chest X Ray

BACKGROUND: Chest X-ray imaging has been proved as a powerful diagnostic method to detect and diagnose COVID-19 cases due to its easy accessibility, lower cost and rapid imaging time. OBJECTIVE: This study aims to improve efficacy of screening COVID-19 infected patients using chest X-ray images with the help of a developed deep convolutional neural network model (CNN) entitled nCoV-NET. METHODS: To train and to evaluate the performance of the developed model, three datasets were collected from resources of “ChestX-ray14”, “COVID-19 image data collection”, and “Chest X-ray collection from Indiana University,” respectively. Overall, 299 COVID-19 pneumonia cases and 1,522 non-COVID 19 cases are involved in this study. To overcome the probable bias due to the unbalanced cases in two classes of the datasets, ResNet, DenseNet, and VGG architectures were re-trained in the fine-tuning stage of the process to distinguish COVID-19 classes using a transfer learning method. Lastly, the optimized final nCoV-NET model was applied to the testing dataset to verify the performance of the proposed model. RESULTS: Although the performance parameters of all re-trained architectures were determined close to each other, the final nCOV-NET model optimized by using DenseNet-161 architecture in the transfer learning stage exhibits the highest performance for classification of COVID-19 cases with the accuracy of 97.1 %. The Activation Mapping method was used to create activation maps that highlights the crucial areas of the radiograph to improve causality and intelligibility. CONCLUSION: This study demonstrated that the proposed CNN model called nCoV-NET can be utilized for reliably detecting COVID-19 cases using chest X-ray images to accelerate the triaging and save critical time for disease control as well as assisting the radiologist to validate their initial diagnosis.

scholarly journals Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 624
Author(s):  
Stefan Rohrmanstorfer ◽  
Mikhail Komarov ◽  
Felix Mödritscher
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Image Data ◽  
Classification Model ◽  
Upper Body ◽  
Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

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