scholarly journals Automated Bone Age Assessment with Image Registration Using Hand X-ray Images

2020 ◽  
Vol 10 (20) ◽  
pp. 7233
Author(s):  
Mohd Asyraf Zulkifley ◽  
Siti Raihanah Abdani ◽  
Nuraisyah Hani Zulkifley
Keyword(s):  
Image Registration ◽  
Mean Squared Error ◽  
Bone Age ◽  
Absolute Error ◽  
Input Image ◽  
Training Dataset ◽  
Negative Consequences ◽  
X Ray ◽  
Convolution Model ◽  
Growth Disorder

One of the methods for identifying growth disorder is by assessing the skeletal bone age. A child with a healthy growth rate will have approximately the same chronological and bone ages. It is important to detect any growth disorder as early as possible, so that mitigation treatment can be administered with less negative consequences. Recently, the most popular approach in assessing the discrepancy between bone and chronological ages is through the subjective protocol of Tanner–Whitehouse that assesses selected regions in the hand X-ray images. This approach relies heavily on the medical personnel experience, which produces a high intra-observer bias. Therefore, an automated bone age prediction system with image registration using hand X-ray images is proposed in order to complement the inexperienced doctors by providing the second opinion. The system relies on an optimized regression network using a novel residual separable convolution model. The regressor network requires an input image to be 299 × 299 pixels, which will be mapped to the predicted bone age through three modules of the Xception network. Moreover, the images will be pre-processed or registered first to a standardized and normalized pose using separable convolutional neural networks. Three steps image registration are performed by segmenting the hand regions, which will be rotated using angle calculated from four keypoints of interest, before positional alignment is applied to ensure the region of interest is located in the middle. The hand segmentation is based on DeepLab V3 plus architecture, while keypoints regressor for angle alignment is based on MobileNet V1 architecture, where both of them use separable convolution as the core operators. To avoid the pitfall of underfitting, synthetic data are generated while using various rotation angles, zooming factors, and shearing images in order to augment the training dataset. The experimental results show that the proposed method returns the lowest mean absolute error and mean squared error of 8.200 months and 121.902 months2, respectively. Hence, an error of less than one year is acceptable in predicting the bone age, which can serve as a good supplement tool for providing the second expert opinion. This work does not consider gender information, which is crucial in making a better prediction, as the male and female bone structures are naturally different.

scholarly journals A Room Monitoring System Using Deep Learning and Perspective Correction Techniques

2020 ◽  
Vol 10 (13) ◽  
pp. 4423
Author(s):  
Huu-Huy Ngo ◽  
Feng-Cheng Lin ◽  
Yang-Ting Sehn ◽  
Mengru Tu ◽  
Chyi-Ren Dow
Keyword(s):  
Deep Learning ◽  
Monitoring System ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Input Image ◽  
Detection Scheme ◽  
Person Detection ◽  
Monitoring Camera ◽  
Bounding Boxes ◽  
2D Images

Studies on room monitoring have only focused on objects in a singular and uniform posture or low-density groups. Considering the wide use of convolutional neural networks for object detection, especially person detection, we use deep learning and perspective correction techniques to propose a room monitoring system that can detect persons with different motion states, high-density groups, and small-sized persons owing to the distance from the camera. This system uses consecutive frames from the monitoring camera as input images. Two approaches are used: perspective correction and person detection. First, perspective correction is used to transform an input image into a 2D top-view image. This allows users to observe the system more easily with different views (2D and 3D views). Second, the proposed person detection scheme combines the Mask region-based convolutional neural network (R-CNN) scheme and the tile technique for person detection, especially for detecting small-sized persons. All results are stored in a cloud database. Moreover, new person coordinates in 2D images are generated from the final bounding boxes and heat maps are created according to the 2D images; these enable users to examine the system quickly in different views. Additionally, a system prototype is developed to demonstrate the feasibility of the proposed system. Experimental results prove that our proposed system outperforms existing schemes in terms of accuracy, mean absolute error (MAE), and root mean squared error (RMSE).

scholarly journals EVALUATION OF ADVANCED DATA MINING ALGORITHMS IN LAND USE/LAND COVER MAPPING

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 283-289
Author(s):  
A. Jamali ◽  
A. Abdul Rahman
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Training Dataset ◽  
Land Cover Mapping ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Data Mining Algorithms ◽  
Simple Logistic

Abstract. For environmental monitoring, land-cover mapping, and urban planning, remote sensing is an effective method. In this paper, firstly, for land use land cover mapping, Landsat 8 OLI image classification based on six advanced mathematical algorithms of machine learning including Random Forest, Decision Table, DTNB, Multilayer Perceptron, Non-Nested Generalized Exemplars (NN ge) and Simple Logistic is used. Then, results are compared in the terms of Overall Accuracy (OA), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for land use land cover (LULC) mapping. Based on the training and test datasets, Simple Logistic had the best performance in terms of OA, MAE and RMSE values of 99.9293, 0.0006 and 0.016 for training dataset and values of 99.9467, 0.0005 and 0.0153 for the test dataset.

scholarly journals Metaheuristic Approaches Integrated with ANN in Forecasting Daily Emergency Department Visits

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Engin Pekel ◽  
Muhammet Gul ◽  
Erkan Celik ◽  
Samuel Yousefi
Keyword(s):  
Mean Squared Error ◽  
Absolute Error ◽  
Emergency Department Visits ◽  
Training Dataset ◽  
Percentage Error ◽  
Quality Level ◽  
Ann Model ◽  
Squared Error ◽  
Ed Visits ◽  
Artificial Neural Network Ann

The overall service quality level of Emergency Departments (EDs) can be improved by accurate forecasting of patient visits. Accordingly, this study aims to evaluate the use of three metaheuristic approaches integrated with Artificial Neural Network (ANN) in forecasting daily ED visits. To do this, five performance measures are used for evaluating the accuracy of the proposed approaches, including Bayesian ANN, Genetic Algorithm-based ANN (GA-ANN), and Particle Swarm Optimization algorithm-based ANN (PSO-ANN). The outputs of this study show that the PSO-ANN model provides the most dominant performance in both the training and testing process. The lowest error is obtained with a mean absolute percentage error (MAPE) of 6.3%, Mean Absolute Error (MAE) of 42.797, Mean Squared Error (MSE) of 2499.340, Root Mean Square Error (RMSE) of 49.933, and R-squared (R2) of 0.824 on the training dataset. The lowest error with an MAPE of 6.0%, MAE of 40.888, MSE of 2839.998, RMSE of 53.292, and R2 of 0.791 is also obtained on the testing process.

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 Evaluation of the feasibility of explainable computer-aided detection of cardiomegaly on chest radiographs using deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mu Sook Lee ◽  
Yong Soo Kim ◽  
Minki Kim ◽  
Muhammad Usman ◽  
Shi Sub Byon ◽  
...  
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Absolute Error ◽  
Training Dataset ◽  
Computer Aided Detection ◽  
Test Dataset ◽  
Cardiothoracic Ratio ◽  
Computer Aided ◽  
Chest X Ray ◽  
Public Datasets

AbstractWe examined the feasibility of explainable computer-aided detection of cardiomegaly in routine clinical practice using segmentation-based methods. Overall, 793 retrospectively acquired posterior–anterior (PA) chest X-ray images (CXRs) of 793 patients were used to train deep learning (DL) models for lung and heart segmentation. The training dataset included PA CXRs from two public datasets and in-house PA CXRs. Two fully automated segmentation-based methods using state-of-the-art DL models for lung and heart segmentation were developed. The diagnostic performance was assessed and the reliability of the automatic cardiothoracic ratio (CTR) calculation was determined using the mean absolute error and paired t-test. The effects of thoracic pathological conditions on performance were assessed using subgroup analysis. One thousand PA CXRs of 1000 patients (480 men, 520 women; mean age 63 ± 23 years) were included. The CTR values derived from the DL models and diagnostic performance exhibited excellent agreement with reference standards for the whole test dataset. Performance of segmentation-based methods differed based on thoracic conditions. When tested using CXRs with lesions obscuring heart borders, the performance was lower than that for other thoracic pathological findings. Thus, segmentation-based methods using DL could detect cardiomegaly; however, the feasibility of computer-aided detection of cardiomegaly without human intervention was limited.

scholarly journals A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

2021 ◽  
Vol 13 (14) ◽  
pp. 7612
Author(s):  
Mahdis sadat Jalaee ◽  
Alireza Shakibaei ◽  
Amin GhasemiNejad ◽  
Sayyed Abdolmajid Jalaee ◽  
Reza Derakhshani
Keyword(s):  
Hybrid Method ◽  
Computational Intelligence ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Coal Consumption ◽  
Squared Error ◽  
Artificial Neural ◽  
Socio Economic Variables ◽  
Intelligence Approach

Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

scholarly journals Estimating Physical Activity Energy Expenditure Using an Ensemble Model-Based Patch-Type Sensor Module

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 861
Author(s):  
Kyeung Ho Kang ◽  
Mingu Kang ◽  
Siho Shin ◽  
Jaehyo Jung ◽  
Meina Li
Keyword(s):  
Physical Activity ◽  
Energy Expenditure ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Ensemble Model ◽  
Direct Calorimetry ◽  
Patch Type ◽  
Mortality And Morbidity ◽  
Squared Error ◽  
Sensor Module

Chronic diseases, such as coronary artery disease and diabetes, are caused by inadequate physical activity and are the leading cause of increasing mortality and morbidity rates. Direct calorimetry by calorie production and indirect calorimetry by energy expenditure (EE) has been regarded as the best method for estimating the physical activity and EE. However, this method is inconvenient, owing to the use of an oxygen respiration measurement mask. In this study, we propose a model that estimates physical activity EE using an ensemble model that combines artificial neural networks and genetic algorithms using the data acquired from patch-type sensors. The proposed ensemble model achieved an accuracy of more than 92% (Root Mean Squared Error (RMSE) = 0.1893, R2 = 0.91, Mean Squared Error (MSE) = 0.014213, Mean Absolute Error (MAE) = 0.14020) by testing various structures through repeated experiments.

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 A hybrid recommender system based-on link prediction for movie baskets analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mohammadsadegh Vahidi Farashah ◽  
Akbar Etebarian ◽  
Reza Azmi ◽  
Reza Ebrahimzadeh Dastjerdi
Keyword(s):  
Recommender System ◽  
Link Prediction ◽  
Mean Squared Error ◽  
Spatial Clustering ◽  
Randomized Algorithm ◽  
Similarity Criterion ◽  
Absolute Error ◽  
Similarity Criteria ◽  
Online Systems ◽  
Squared Error

AbstractOver the past decade, recommendation systems have been one of the most sought after by various researchers. Basket analysis of online systems’ customers and recommending attractive products (movies) to them is very important. Providing an attractive and favorite movie to the customer will increase the sales rate and ultimately improve the system. Various methods have been proposed so far to analyze customer baskets and offer entertaining movies but each of the proposed methods has challenges, such as lack of accuracy and high error of recommendations. In this paper, a link prediction-based method is used to meet the challenges of other methods. The proposed method in this paper consists of four phases: (1) Running the CBRS that in this phase, all users are clustered using Density-based spatial clustering of applications with noise algorithm (DBScan), and classification of new users using Deep Neural Network (DNN) algorithm. (2) Collaborative Recommender System (CRS) Based on Hybrid Similarity Criterion through which similarities are calculated based on a threshold (lambda) between the new user and the users in the selected category. Similarity criteria are determined based on age, gender, and occupation. The collaborative recommender system extracts users who are the most similar to the new user. Then, the higher-rated movie services are suggested to the new user based on the adjacency matrix. (3) Running improved Friendlink algorithm on the dataset to calculate the similarity between users who are connected through the link. (4) This phase is related to the combination of collaborative recommender system’s output and improved Friendlink algorithm. The results show that the Mean Squared Error (MSE) of the proposed model has decreased respectively 8.59%, 8.67%, 8.45% and 8.15% compared to the basic models such as Naive Bayes, multi-attribute decision tree and randomized algorithm. In addition, Mean Absolute Error (MAE) of the proposed method decreased by 4.5% compared to SVD and approximately 4.4% compared to ApproSVD and Root Mean Squared Error (RMSE) of the proposed method decreased by 6.05 % compared to SVD and approximately 6.02 % compared to ApproSVD.

scholarly journals Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Isabella Castiglioni ◽  
Davide Ippolito ◽  
Matteo Interlenghi ◽  
Caterina Beatrice Monti ◽  
Christian Salvatore ◽  
...  
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Clinical Information ◽  
Training Dataset ◽  
Independent Dataset ◽  
X Ray ◽  
Learning Classifier ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Deep Learning Model

Abstract Background We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2. Conclusions This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

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