A robust framework for shoulder implant X-ray image classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Minh Thanh Vo ◽  
Anh H. Vo ◽  
Tuong Le
Keyword(s):  
Image Classification ◽  
Performance Metrics ◽  
State Of The Art ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Extraction Process ◽  
Content Type ◽  
X Ray ◽  
Robust Model ◽  
Input Dataset

PurposeMedical images are increasingly popular; therefore, the analysis of these images based on deep learning helps diagnose diseases become more and more essential and necessary. Recently, the shoulder implant X-ray image classification (SIXIC) dataset that includes X-ray images of implanted shoulder prostheses produced by four manufacturers was released. The implant's model detection helps to select the correct equipment and procedures in the upcoming surgery.Design/methodology/approachThis study proposes a robust model named X-Net to improve the predictability for shoulder implants X-ray image classification in the SIXIC dataset. The X-Net model utilizes the Squeeze and Excitation (SE) block integrated into Residual Network (ResNet) module. The SE module aims to weigh each feature map extracted from ResNet, which aids in improving the performance. The feature extraction process of X-Net model is performed by both modules: ResNet and SE modules. The final feature is obtained by incorporating the extracted features from the above steps, which brings more important characteristics of X-ray images in the input dataset. Next, X-Net uses this fine-grained feature to classify the input images into four classes (Cofield, Depuy, Zimmer and Tornier) in the SIXIC dataset.FindingsExperiments are conducted to show the proposed approach's effectiveness compared with other state-of-the-art methods for SIXIC. The experimental results indicate that the approach outperforms the various experimental methods in terms of several performance metrics. In addition, the proposed approach provides the new state of the art results in all performance metrics, such as accuracy, precision, recall, F1-score and area under the curve (AUC), for the experimental dataset.Originality/valueThe proposed method with high predictive performance can be used to assist in the treatment of injured shoulder joints.

An experimental study on feature-based SLAM for multi-legged robots with RGB-D sensors

2017 ◽  
Vol 44 (4) ◽  
pp. 428-441 ◽  
Author(s):  
Michał R. Nowicki ◽  
Dominik Belter ◽  
Aleksander Kostusiak ◽  
Petr Cížek ◽  
Jan Faigl ◽  
...  
Keyword(s):  
Performance Metrics ◽  
State Of The Art ◽  
Building Blocks ◽  
Bundle Adjustment ◽  
The State ◽  
Legged Robots ◽  
Evaluation Methodology ◽  
Walking Robots ◽  
Content Type ◽  
Feature Based

Purpose This paper aims to evaluate four different simultaneous localization and mapping (SLAM) systems in the context of localization of multi-legged walking robots equipped with compact RGB-D sensors. This paper identifies problems related to in-motion data acquisition in a legged robot and evaluates the particular building blocks and concepts applied in contemporary SLAM systems against these problems. The SLAM systems are evaluated on two independent experimental set-ups, applying a well-established methodology and performance metrics. Design/methodology/approach Four feature-based SLAM architectures are evaluated with respect to their suitability for localization of multi-legged walking robots. The evaluation methodology is based on the computation of the absolute trajectory error (ATE) and relative pose error (RPE), which are performance metrics well-established in the robotics community. Four sequences of RGB-D frames acquired in two independent experiments using two different six-legged walking robots are used in the evaluation process. Findings The experiments revealed that the predominant problem characteristics of the legged robots as platforms for SLAM are the abrupt and unpredictable sensor motions, as well as oscillations and vibrations, which corrupt the images captured in-motion. The tested adaptive gait allowed the evaluated SLAM systems to reconstruct proper trajectories. The bundle adjustment-based SLAM systems produced best results, thanks to the use of a map, which enables to establish a large number of constraints for the estimated trajectory. Research limitations/implications The evaluation was performed using indoor mockups of terrain. Experiments in more natural and challenging environments are envisioned as part of future research. Practical implications The lack of accurate self-localization methods is considered as one of the most important limitations of walking robots. Thus, the evaluation of the state-of-the-art SLAM methods on legged platforms may be useful for all researchers working on walking robots’ autonomy and their use in various applications, such as search, security, agriculture and mining. Originality/value The main contribution lies in the integration of the state-of-the-art SLAM methods on walking robots and their thorough experimental evaluation using a well-established methodology. Moreover, a SLAM system designed especially for RGB-D sensors and real-world applications is presented in details.

Better Practices in the Development and Validation of Recidivism Risk Assessments: The Minnesota Sex Offender Screening Tool–4

2017 ◽  
Vol 30 (4) ◽  
pp. 538-564 ◽  
Author(s):  
Grant Duwe
Keyword(s):  
Sex Offenders ◽  
Sex Offender ◽  
Screening Tool ◽  
Performance Metrics ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Test Set ◽  
And Performance ◽  
Logistic Regression Algorithm ◽  
Development And Validation

This study examines the development and validation of the Minnesota Sex Offender Screening Tool–4 (MnSOST-4) on a dataset consisting of 5,745 sex offenders released from Minnesota prisons between 2003 and 2012. Bootstrap resampling was used to select predictors, and k-fold and split-sample methods were used to internally validate the MnSOST-4. Using sex offense reconviction within 4 years of release from prison as the failure criterion, the data showed that 130 (2.3%) offenders in the overall sample were recidivists. Multiple classification methods and performance metrics were used to develop the MnSOST-4 and evaluate its predictive performance on the test set. The results from the regularized logistic regression algorithm showed that the MnSOST-4 performed well in predicting sexual recidivism in the test set, achieving an area under the curve (AUC) of 0.835. Additional analyses on the test set revealed that the MnSOST-4 outperformed the Minnesota Sex Offender Screening Tool–3 (MnSOST-3), Minnesota Sex Offender Screening Tool–Revised (MnSOST-R), and Static-99 in predicting sexual reoffending.

scholarly journals The implication of machine learning for financial solvency prediction: an empirical analysis on public listed companies of Bangladesh

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Abdullah
Keyword(s):  
Machine Learning ◽  
Enterprise Resource Planning ◽  
Model Building ◽  
Performance Metrics ◽  
Financial Institution ◽  
Area Under The Curve ◽  
Resource Planning ◽  
Listed Companies ◽  
Content Type ◽  
Artificial Neural Network Classifier

PurposeFinancial health of a corporation is a great concern for every investor level and decision-makers. For many years, financial solvency prediction is a significant issue throughout academia, precisely in finance. This requirement leads this study to check whether machine learning can be implemented in financial solvency prediction.Design/methodology/approachThis study analyzed 244 Dhaka stock exchange public-listed companies over the 2015–2019 period, and two subsets of data are also developed as training and testing datasets. For machine learning model building, samples are classified as secure, healthy and insolvent by the Altman Z-score. R statistical software is used to make predictive models of five classifiers and all model performances are measured with different performance metrics such as logarithmic loss (logLoss), area under the curve (AUC), precision recall AUC (prAUC), accuracy, kappa, sensitivity and specificity.FindingsThis study found that the artificial neural network classifier has 88% accuracy and sensitivity rate; also, AUC for this model is 96%. However, the ensemble classifier outperforms all other models by considering logLoss and other metrics.Research limitations/implicationsThe major result of this study can be implicated to the financial institution for credit scoring, credit rating and loan classification, etc. And other companies can implement machine learning models to their enterprise resource planning software to trace their financial solvency.Practical implicationsFinally, a predictive application is developed through training a model with 1,200 observations and making it available for all rational and novice investors (Abdullah, 2020).Originality/valueThis study found that, with the best of author expertise, the author did not find any studies regarding machine learning research of financial solvency that examines a comparable number of a dataset, with all these models in Bangladesh.

scholarly journals X-ray Image Classification Using State-of-the-art Networks

2021 ◽  
Author(s):  
Hamid Hassanpour
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
State Of The Art ◽  
Learning Models ◽  
X Ray

In this article, State-of-the-art deep learning models are evaluated and their performances in X-ray image classification is reported.

scholarly journals X-ray Image Classification Using State-of-the-art Networks

2021 ◽  
Author(s):  
Hamid Hassanpour
Keyword(s):  
Deep Learning ◽  
Image Classification ◽  
State Of The Art ◽  
Learning Models ◽  
X Ray

In this article, State-of-the-art deep learning models are evaluated and their performances in X-ray image classification is reported.

Improving the performance of the intrusion detection systems by the machine learning explainability

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Quang-Vinh Dang
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
State Of The Art ◽  
Predictive Performance ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Content Type ◽  
Detection Systems ◽  
Experimental Works ◽  
The Relationship

Purpose This study aims to explain the state-of-the-art machine learning models that are used in the intrusion detection problem for human-being understandable and study the relationship between the explainability and the performance of the models. Design/methodology/approach The authors study a recent intrusion data set collected from real-world scenarios and use state-of-the-art machine learning algorithms to detect the intrusion. The authors apply several novel techniques to explain the models, then evaluate manually the explanation. The authors then compare the performance of model post- and prior-explainability-based feature selection. Findings The authors confirm our hypothesis above and claim that by forcing the explainability, the model becomes more robust, requires less computational power but achieves a better predictive performance. Originality/value The authors draw our conclusions based on their own research and experimental works.

scholarly journals Deep-Pneumonia Framework Using Deep Learning Models Based on Chest X-Ray Images

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 649 ◽  
Author(s):  
Nada M. Elshennawy ◽  
Dina M. Ibrahim
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Area Under The Curve ◽  
The Elderly ◽  
Learning Models ◽  
X Ray ◽  
Learning Framework ◽  
Chest X Ray ◽  
Long Short Term Memory

Pneumonia is a contagious disease that causes ulcers of the lungs, and is one of the main reasons for death among children and the elderly in the world. Several deep learning models for detecting pneumonia from chest X-ray images have been proposed. One of the extreme challenges has been to find an appropriate and efficient model that meets all performance metrics. Proposing efficient and powerful deep learning models for detecting and classifying pneumonia is the main purpose of this work. In this paper, four different models are developed by changing the used deep learning method; two pre-trained models, ResNet152V2 and MobileNetV2, a Convolutional Neural Network (CNN), and a Long Short-Term Memory (LSTM). The proposed models are implemented and evaluated using Python and compared with recent similar research. The results demonstrate that our proposed deep learning framework improves accuracy, precision, F1-score, recall, and Area Under the Curve (AUC) by 99.22%, 99.43%, 99.44%, 99.44%, and 99.77%, respectively. As clearly illustrated from the results, the ResNet152V2 model outperforms other recently proposed works. Moreover, the other proposed models—MobileNetV2, CNN, and LSTM-CNN—achieved results with more than 91% in accuracy, recall, F1-score, precision, and AUC, and exceed the recently introduced models in the literature.

scholarly journals Detecting Pneumonia Using Convolutions and Dynamic Capsule Routing for Chest X-ray Images

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1068 ◽  
Author(s):  
Ansh Mittal ◽  
Deepika Kumar ◽  
Mamta Mittal ◽  
Tanzila Saba ◽  
Ibrahim Abunadi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Simple Model ◽  
Image Classification ◽  
State Of The Art ◽  
Test Accuracy ◽  
Learning Models ◽  
X Ray ◽  
Chest X Ray ◽  
Classification Tasks

An entity’s existence in an image can be depicted by the activity instantiation vector from a group of neurons (called capsule). Recently, multi-layered capsules, called CapsNet, have proven to be state-of-the-art for image classification tasks. This research utilizes the prowess of this algorithm to detect pneumonia from chest X-ray (CXR) images. Here, an entity in the CXR image can help determine if the patient (whose CXR is used) is suffering from pneumonia or not. A simple model of capsules (also known as Simple CapsNet) has provided results comparable to best Deep Learning models that had been used earlier. Subsequently, a combination of convolutions and capsules is used to obtain two models that outperform all models previously proposed. These models—Integration of convolutions with capsules (ICC) and Ensemble of convolutions with capsules (ECC)—detect pneumonia with a test accuracy of 95.33% and 95.90%, respectively. The latter model is studied in detail to obtain a variant called EnCC, where n = 3, 4, 8, 16. Here, the E4CC model works optimally and gives test accuracy of 96.36%. All these models had been trained, validated, and tested on 5857 images from Mendeley.

scholarly journals A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

2021 ◽  
Vol 18 (13) ◽  
pp. 6724
Author(s):  
Yaghoub Pourasad ◽  
Fausto Cavallaro
Keyword(s):  
Image Processing ◽  
Performance Metrics ◽  
State Of The Art ◽  
Use Case ◽  
X Ray ◽  
Image Processing Techniques ◽  
Processing Approach ◽  
Comparison Results ◽  
Processing Techniques

At present, there is an increase in the capacity of data generated and stored in the medical area. Thus, for the efficient handling of these extensive data, the compression methods need to be re-explored by considering the algorithm’s complexity. To reduce the redundancy of the contents of the image, thus increasing the ability to store or transfer information in optimal form, an image processing approach needs to be considered. So, in this study, two compression techniques, namely lossless compression and lossy compression, were applied for image compression, which preserves the image quality. Moreover, some enhancing techniques to increase the quality of a compressed image were employed. These methods were investigated, and several comparison results are demonstrated. Finally, the performance metrics were extracted and analyzed based on state-of-the-art methods. PSNR, MSE, and SSIM are three performance metrics that were used for the sample medical images. Detailed analysis of the measurement metrics demonstrates better efficiency than the other image processing techniques. This study helps to better understand these strategies and assists researchers in selecting a more appropriate technique for a given use case.

Revisiting the High-Pressure Properties of the Metal-Organic Frameworks ZIF-8 and ZIF-67

2020 ◽  
Author(s):  
Pia Vervoorts ◽  
Stefan Burger ◽  
Karina Hemmer ◽  
Gregor Kieslich
Keyword(s):  
High Pressure ◽  
State Of The Art ◽  
Energy Landscape ◽  
Structural Flexibility ◽  
Zeolitic Imidazolate Frameworks ◽  
Stimuli Responsive ◽  
X Ray Diffraction ◽  
X Ray ◽  
Open Questions ◽  
Metal Organic

The zeolitic imidazolate frameworks ZIF-8 and ZIF-67 harbour a series of fascinating stimuli responsive properties. Looking at their responsitivity to hydrostatic pressure as stimulus, open questions exist regarding the isotropic compression with non-penetrating pressure transmitting media. By applying a state-of-the-art high-pressure powder X-ray diffraction setup, we revisit the high-pressure behaviour of ZIF-8 and ZIF-67 up to <i>p</i> = 0.4 GPa in small pressure increments. We observe a drastic, reversible change of high-pressure powder X-ray diffraction data at <i>p</i> = 0.3 GPa, discovering large volume structural flexibility in ZIF-8 and ZIF-67. Our results imply a shallow underlying energy landscape in ZIF-8 and ZIF-67, an observation that might point at rich polymorphism of ZIF-8 and ZIF-67, similar to ZIF-4(Zn).<br>

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