Using Convolutional Neural Network with Taguchi Parametric Optimization for Knee Segmentation from X-Ray Images

Loss of knee cartilage can cause intense pain at the knee epiphysis and this is one of the most common diseases worldwide. To diagnose this condition, the distance between the femur and tibia is calculated based on X-ray images. Accurate segmentation of the femur and tibia is required to assist in the calculation process. Several studies have investigated the use of automatic knee segmentation to assist in the calculation process, but the results are of limited value owing to the complexity of the knee. To address this problem, this study exploits deep learning for robust segmentation not affected by the environment. In addition, the Taguchi method is applied to optimize the deep learning results. Deep learning architecture, optimizer, and learning rate are considered for the Taguchi table to check the impact and interaction of the results. When the Dilated-Resnet architecture is used with the Adam optimizer and a learning rate of 0.001, dice coefficients of 0.964 and 0.942 are obtained for the femur and tibia for knee segmentation. The implemented procedure and the results of this investigation may be beneficial to help in determining the correct margins for the femur and tibia and can be the basis for developing an automatic diagnosis algorithm for orthopedic diseases.

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Using a Deep Learning Model to Explore the Impact of Clinical Data on COVID-19 Diagnosis Using Chest X-ray

Sensors ◽

10.3390/s22020669 ◽

2022 ◽

Vol 22 (2) ◽

pp. 669

Author(s):

Irfan Ullah Khan ◽

Nida Aslam ◽

Talha Anwar ◽

Hind S. Alsaif ◽

Sara Mhd. Bachar Chrouf ◽

...

Keyword(s):

Deep Learning ◽

Clinical Data ◽

Disease Diagnosis ◽

University Hospital ◽

X Ray ◽

Clinical Patient ◽

Proposed Model ◽

Chest X Ray ◽

The Impact ◽

Deep Learning Model

The coronavirus pandemic (COVID-19) is disrupting the entire world; its rapid global spread threatens to affect millions of people. Accurate and timely diagnosis of COVID-19 is essential to control the spread and alleviate risk. Due to the promising results achieved by integrating machine learning (ML), particularly deep learning (DL), in automating the multiple disease diagnosis process. In the current study, a model based on deep learning was proposed for the automated diagnosis of COVID-19 using chest X-ray images (CXR) and clinical data of the patient. The aim of this study is to investigate the effects of integrating clinical patient data with the CXR for automated COVID-19 diagnosis. The proposed model used data collected from King Fahad University Hospital, Dammam, KSA, which consists of 270 patient records. The experiments were carried out first with clinical data, second with the CXR, and finally with clinical data and CXR. The fusion technique was used to combine the clinical features and features extracted from images. The study found that integrating clinical data with the CXR improves diagnostic accuracy. Using the clinical data and the CXR, the model achieved an accuracy of 0.970, a recall of 0.986, a precision of 0.978, and an F-score of 0.982. Further validation was performed by comparing the performance of the proposed system with the diagnosis of an expert. Additionally, the results have shown that the proposed system can be used as a tool that can help the doctors in COVID-19 diagnosis.

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Detection of COVID-19 and Other Pneumonia Cases Using Convolutional Neural Networks and X-ray Images

Ingeniería e Investigación ◽

10.15446/ing.investig.v42n1.90289 ◽

2021 ◽

Vol 42 (1) ◽

pp. e90289

Author(s):

Carlos Eduardo Belman López

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Network Capacity ◽

Superior Performance ◽

Data Set ◽

X Ray ◽

Regularization Techniques ◽

The Impact

Given that it is fundamental to detect positive COVID-19 cases and treat affected patients quickly to mitigate the impact of the virus, X-ray images have been subjected to research regarding COVID-19, together with deep learning models, eliminating disadvantages such as the scarcity of RT-PCR test kits, their elevated costs, and the long wait for results. The contribution of this paper is to present new models for detecting COVID-19 and other cases of pneumonia using chest X-ray images and convolutional neural networks, thus providing accurate diagnostics in binary and 4-classes classification scenarios. Classification accuracy was improved, and overfitting was prevented by following 2 actions: (1) increasing the data set size while the classification scenarios were balanced; and (2) adding regularization techniques and performing hyperparameter optimization. Additionally, the network capacity and size in the models were reduced as much as possible, making the final models a perfect option to be deployed locally on devices with limited capacities and without the need for Internet access. The impact of key hyperparameters was tested using modern deep learning packages. The final models obtained a classification accuracy of 99,17 and 94,03% for the binary and categorical scenarios, respectively, achieving superior performance compared to other studies in the literature, and requiring a significantly lower number of parameters. The models can also be placed on a digital platform to provide instantaneous diagnostics and surpass the shortage of experts and radiologists.

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The Effectiveness of Image Augmentation in Deep Learning Networks for Detecting COVID-19: A Geometric Transformation Perspective

Frontiers in Medicine ◽

10.3389/fmed.2021.629134 ◽

2021 ◽

Vol 8 ◽

Author(s):

Mohamed Elgendi ◽

Muhammad Umer Nasir ◽

Qunfeng Tang ◽

David Smith ◽

John-Paul Grenier ◽

...

Keyword(s):

Deep Learning ◽

Data Augmentation ◽

Learning Algorithms ◽

Training Dataset ◽

Geometric Transformation ◽

Detection Accuracy ◽

X Rays ◽

X Ray ◽

Testing Dataset ◽

The Impact

Chest X-ray imaging technology used for the early detection and screening of COVID-19 pneumonia is both accessible worldwide and affordable compared to other non-invasive technologies. Additionally, deep learning methods have recently shown remarkable results in detecting COVID-19 on chest X-rays, making it a promising screening technology for COVID-19. Deep learning relies on a large amount of data to avoid overfitting. While overfitting can result in perfect modeling on the original training dataset, on a new testing dataset it can fail to achieve high accuracy. In the image processing field, an image augmentation step (i.e., adding more training data) is often used to reduce overfitting on the training dataset, and improve prediction accuracy on the testing dataset. In this paper, we examined the impact of geometric augmentations as implemented in several recent publications for detecting COVID-19. We compared the performance of 17 deep learning algorithms with and without different geometric augmentations. We empirically examined the influence of augmentation with respect to detection accuracy, dataset diversity, augmentation methodology, and network size. Contrary to expectation, our results show that the removal of recently used geometrical augmentation steps actually improved the Matthews correlation coefficient (MCC) of 17 models. The MCC without augmentation (MCC = 0.51) outperformed four recent geometrical augmentations (MCC = 0.47 for Data Augmentation 1, MCC = 0.44 for Data Augmentation 2, MCC = 0.48 for Data Augmentation 3, and MCC = 0.49 for Data Augmentation 4). When we retrained a recently published deep learning without augmentation on the same dataset, the detection accuracy significantly increased, with a χMcNemar′s statistic2=163.2 and a p-value of 2.23 × 10−37. This is an interesting finding that may improve current deep learning algorithms using geometrical augmentations for detecting COVID-19. We also provide clinical perspectives on geometric augmentation to consider regarding the development of a robust COVID-19 X-ray-based detector.

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Optimal Accuracy Zone Identification in Object Detection Technique - A Learning Rate Methodology

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a2258.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 6470-6476

Keyword(s):

Deep Learning ◽

Object Detection ◽

Learning Rate ◽

Training Data ◽

Fine Tuning ◽

Batch Size ◽

Detection Accuracy ◽

Learning Models ◽

The Impact ◽

Selection Of

In the recent past, Deep Learning models [1] are predominantly being used in Object Detection algorithms due to their accurate Image Recognition capability. These models extract features from the input images and videos [2] for identification of objects present in them. Various applications of these models include Image Processing, Video analysis, Speech Recognition, Biomedical Image Analysis, Biometric Recognition, Iris Recognition, National Security applications, Cyber Security, Natural Language Processing [3], Weather Forecasting applications, Renewable Energy Generation Scheduling etc. These models utilize the concept of Convolutional Neural Network (CNN) [3], which constitutes several layers of artificial neurons. The accuracy of Deep Learning models [1] depends on various parameters such as ‘Learning-rate’, ‘Training batch size’, ‘Validation batch size’, ‘Activation Function’, ‘Drop-out rate’ etc. These parameters are known as Hyper-Parameters. Object detection accuracy depends on selection of Hyperparameters and these in-turn decides the optimum accuracy. Hence, finding the best values for these parameters is a challenging task. Fine-Tuning is a process used for selection of a suitable Hyper-Parameter value for improvement of object detection accuracy. Selection of an inappropriate Hyper-Parameter value, leads to Over-Fitting or Under-Fitting of data. Over-Fitting is a case, when training data is larger than the required, which results in learning noise and inaccurate object detection. Under-fitting is a case, when the model is unable to capture the trend of the data and which leads to more erroneous results in testing or training data. In this paper, a balance between Over-fitting and Under-fitting is achieved by varying the ‘Learning rate’ of various Deep Learning models. Four Deep Learning Models such as VGG16, VGG19, InceptionV3 and Xception are considered in this paper for analysis purpose. The best zone of Learning-rate for each model, in respect of maximum Object Detection accuracy, is analyzed. In this paper a dataset of 70 object classes is taken and the prediction accuracy is analyzed by changing the ‘Learning-rate’ and keeping the rest of the Hyper-Parameters constant. This paper mainly concentrates on the impact of ‘Learning-rate’ on accuracy and identifies an optimum accuracy zone in Object Detection

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Multisource Smart Computer-Aided System for Mining COVID-19 Infection Data

Healthcare ◽

10.3390/healthcare10010109 ◽

2022 ◽

Vol 10 (1) ◽

pp. 109

Author(s):

Mohammad T. Abou-Kreisha ◽

Humam K. Yaseen ◽

Khaled A. Fathy ◽

Ebeid A. Ebeid ◽

Kamal A. ElDahshan

Keyword(s):

Deep Learning ◽

Optimal Parameter ◽

Learning Model ◽

Superior Performance ◽

Stochastic Gradient Descent ◽

Detection Rates ◽

X Ray ◽

Infected Case ◽

Computer Aided ◽

The Impact

In this paper, we approach the problem of detecting and diagnosing COVID-19 infections using multisource scan images including CT and X-ray scans to assist the healthcare system during the COVID-19 pandemic. Here, a computer-aided diagnosis (CAD) system is proposed that utilizes analysis of the CT or X-ray to diagnose the impact of damage in the respiratory system per infected case. The CAD was utilized and optimized by hyper-parameters for shallow learning, e.g., SVM and deep learning. For the deep learning, mini-batch stochastic gradient descent was used to overcome fitting problems during transfer learning. The optimal parameter list values were found using the naïve Bayes technique. Our contributions are (i) a comparison among the detection rates of pre-trained CNN models, (ii) a suggested hybrid deep learning with shallow machine learning, (iii) an extensive analysis of the results of COVID-19 transition and informative conclusions through developing various transfer techniques, and (iv) a comparison of the accuracy of the previous models with the systems of the present study. The effectiveness of the proposed CAD is demonstrated using three datasets, either using an intense learning model as a fully end-to-end solution or using a hybrid deep learning model. Six experiments were designed to illustrate the superior performance of our suggested CAD when compared to other similar approaches. Our system achieves 99.94, 99.6, 100, 97.41, 99.23, and 98.94 accuracy for binary and three-class labels for the CT and two CXR datasets.

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Analysis of the impact of parameters in TextGCN

10.14210/cotb.v12.p014-019 ◽

2021 ◽

Author(s):

Henrique Varella Ehrenfried ◽

Eduardo Federal University of Paraná Curitiba, Paraná, Brazil

Keyword(s):

Deep Learning ◽

Side Effect ◽

State Of The Art ◽

The State ◽

Learning Rate ◽

Learning Models ◽

Early Stopping ◽

The Impact

Deep learning models uses many parameters to work properly. Asthey become more complex, the authors of these novel models cannotexplore in their papers the variation of each parameter of theirmodel. Therefore, this work describes an analysis of the impact offour different parameters (Early Stopping, Learning Rate, Dropout,and Hidden 1) in the TextGCN Model. This evaluation used fourdatasets considered in the original TextGCN publication, obtainingas a side-effect small improvements in the results of three of them.The most relevant conclusion is that these parameters influence theconvergence and accuracy, although they individually do not constitutestrong support when aiming to improve the model’s resultsreported as the state-of-the-art.

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Analyzing the Impact of X-Ray Tomography on the Reliability of Integrated Circuits

ISTFA 2015: Conference Proceedings from the 41st International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2015p0154 ◽

2015 ◽

Author(s):

Halit Dogan ◽

Md Mahbub Alam ◽

Navid Asadizanjani ◽

Sina Shahbazmohamadi ◽

Domenic Forte ◽

...

Keyword(s):

Integrated Circuits ◽

Integrated Circuit ◽

3D Imaging ◽

Three Dimensional ◽

Serial Sectioning ◽

Promising Technique ◽

Flash Memories ◽

X Ray ◽

3D Information ◽

The Impact

Abstract X-ray tomography is a promising technique that can provide micron level, internal structure, and three dimensional (3D) information of an integrated circuit (IC) component without the need for serial sectioning or decapsulation. This is especially useful for counterfeit IC detection as demonstrated by recent work. Although the components remain physically intact during tomography, the effect of radiation on the electrical functionality is not yet fully investigated. In this paper we analyze the impact of X-ray tomography on the reliability of ICs with different fabrication technologies. We perform a 3D imaging using an advanced X-ray machine on Intel flash memories, Macronix flash memories, Xilinx Spartan 3 and Spartan 6 FPGAs. Electrical functionalities are then tested in a systematic procedure after each round of tomography to estimate the impact of X-ray on Flash erase time, read margin, and program operation, and the frequencies of ring oscillators in the FPGAs. A major finding is that erase times for flash memories of older technology are significantly degraded when exposed to tomography, eventually resulting in failure. However, the flash and Xilinx FPGAs of newer technologies seem less sensitive to tomography, as only minor degradations are observed. Further, we did not identify permanent failures for any chips in the time needed to perform tomography for counterfeit detection (approximately 2 hours).

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