4 research outputs found
Machine learning for classifying and interpreting coherent X-ray speckle patterns
Speckle patterns produced by coherent X-ray have a close relationship with
the internal structure of materials but quantitative inversion of the
relationship to determine structure from speckle patterns is challenging. Here,
we investigate the link between coherent X-ray speckle patterns and sample
structures using a model 2D disk system and explore the ability of machine
learning to learn aspects of the relationship. Specifically, we train a deep
neural network to classify the coherent X-ray speckle patterns according to the
disk number density in the corresponding structure. It is demonstrated that the
classification system is accurate for both non-disperse and disperse size
distributions
A comparative study for glioma classification using deep convolutional neural networks
Gliomas are a type of central nervous system (CNS) tumor that accounts for the most of
malignant brain tumors. The World Health Organization (WHO) divides gliomas into four grades
based on the degree of malignancy. Gliomas of grades I-II are considered low-grade gliomas (LGGs),
whereas gliomas of grades III-IV are termed high-grade gliomas (HGGs). Accurate classification of
HGGs and LGGs prior to malignant transformation plays a crucial role in treatment planning.
Magnetic resonance imaging (MRI) is the cornerstone for glioma diagnosis. However, examination
of MRI data is a time-consuming process and error prone due to human intervention. In this study we
introduced a custom convolutional neural network (CNN) based deep learning model trained from
scratch and compared the performance with pretrained AlexNet, GoogLeNet and SqueezeNet
through transfer learning for an effective glioma grade prediction. We trained and tested the models
based on pathology-proven 104 clinical cases with glioma (50 LGGs, 54 HGGs). A combination of
data augmentation techniques was used to expand the training data. Five-fold cross-validation was
applied to evaluate the performance of each model. We compared the models in terms of averaged
values of sensitivity, specificity, F1 score, accuracy, and area under the receiver operating
characteristic curve (AUC). According to the experimental results, our custom-design deep CNN
model achieved comparable or even better performance than the pretrained models. Sensitivity,
specificity, F1 score, accuracy and AUC values of the custom model were 0.980, 0.963, 0.970, 0.971
and 0.989, respectively. GoogLeNet showed better performance than AlexNet and SqueezeNet in
terms of accuracy and AUC with a sensitivity, specificity, F1 score, accuracy, and AUC values of
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Mathematical Biosciences and Engineering Volume 18, Issue 2, 1550–1572.
0.980, 0.889, 0.933, 0.933, and 0.987, respectively. AlexNet yielded a sensitivity, specificity, F1
score, accuracy, and AUC values of 0.940, 0.907, 0.922, 0.923 and 0.970, respectively. As for
SqueezeNet, the sensitivity, specificity, F1 score, accuracy, and AUC values were 0.920, 0.870, 0.893,
0.894, and 0.975, respectively. The results have shown the effectiveness and robustness of the
proposed custom model in classifying gliomas into LGG and HGG. The findings suggest that the
deep CNNs and transfer learning approaches can be very useful to solve classification problems in
the medical domain
Prediction of Severity of Aviation Landing Accidents Using Support Vector Machine Models
The purpose of this study was to apply support vector machine (SVM) models to predict the severity of aircraft damage and the severity of personal injury during an aircraft approach and landing accident and to evaluate and rank the importance of 14 accident factors to the severity. Three new factors were introduced using the theory of inattentional blindness: The presence of visual area surface penetrations for a runway, the Federal Aviation Administration’s (FAA) visual area surface penetration policy timeframe, and the type of runway approach lighting.
The study comprised 1,297 aircraft approach and landing accidents at airports within the United States with at least one instrument approach procedure. The dataset was gathered from a combination of the National Transportation Safety Board (NTSB) accident database, the NTSB accident reports, and the FAA’s Instrument Flight Procedure Gateway website. Four SVM models were developed in using the linear, polynomial, radial basis function (RBF), and sigmoid kernels for the severity of aircraft damage and another four SVM models were developed for the severity of personal injury. Five-fold cross-validation was used for testing the model accuracy and measures including evaluation of confusion matrices, misclassification rates, accuracy, precision, sensitivity/recall, and F1-scores for model comparison. The best kernel models were selected and its model hyperparameters were optimized for the best model performance.
The SVM models using the RBF kernel produced the best machine learning models, with a 96% accuracy for predicting the severity of aircraft damage (0.94 precision, 0.95 recall, and 0.95 F1-score) and a 98% accuracy for predicting the severity of personal injury (0.99 precision, 0.98 recall, and 0.99 F1-score). The top predictors across both models were the pilot’s total flight hours, time of the accident, pilot’s age, crosswind component, landing runway number, single-engine land certificate, and any obstacle penetration. Specifically, the visual area surface obstacle penetration status ranked ninth across both SVM models. However, as a sub-category, an obstacle penetration on final approach was the seventh overall predictor and the second highest of the categorical predictors. The FAA visual area surface policy was ranked eighth as the overall factor, and the FAA policy from 2018 to 2019 was the third highest categorical predictor. Finally, the type of runway lighting was the sixth ranked prediction factor.
This study demonstrates the benefit of SVM modeling using the RBF kernel for accident prediction and for datasets with categorical factors. It is recommended for the NTSB to add the collection of all three new factors into the NTSB database for future aviation accident research. Lastly, flight training should include information on a pilot’s susceptibility to inattentional blindness and the risks of potential obstacles in their flight path