Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment

Cömert, Zafer; Kocamaz, Adnan Fatih; Velappan, Subha

doi:10.1016/j.compbiomed.2018.06.003

Cited by 72 publications

(55 citation statements)

References 66 publications

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“…The best performance was achieved in the AlexNet architecture. Attributes were obtained from the AlexNet architecture, decision tree (DT) [12], QDA [13], linear discriminant analysis (LDA) [14], support vector machines (SVM) [15], nearest neighbor (KNN) [16], and Softmax [17] methods using, classification process. Figure 2 shows the model design of this study.…”

Section: Data Set and Methodsmentioning

confidence: 99%

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Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

Toğaçar¹,

Ergen²,

Sertkaya³

2019

ELEKTRON ELEKTROTECH

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The white blood cells produced in the bone marrow and lymphoid tissue known as leucocytes are an important part of the immune system to protect the body against foreign invaders and infectious disease. These cells, which do not have color, have a few days or several weeks of life. A lot of clinic experience is required for a doctor to detect the amount of white blood cells in human blood and classify it. Thus, early and accurate diagnosis can be made in the formation of various disease types, including infection on the immune system, such as anemia and leukemia, while evaluating and determining the disease of a patient. The white blood cells can be separated into four subclasses, such as Eosinophil, Lymphocyte, Monocyte, and Neutrophil. This study focuses on the separation of the white blood cell images by the classification process using convolutional neural network models, which is a deep learning model. A deep learning network, which is slow in the training step due to the complex architecture, but fast in the test step, is used for the feature extraction instead of intricate methods. For the subclass separation of white blood cells, the experimental results show that the AlexNet architecture gives the correct recognition rate among the convolutional neural network architectures tested in the study. Various classifiers are performed on the features derived from the AlexNet architecture to evaluate the classification performance. The best performance in the classification of white blood cells is given by the quadratic discriminant analysis classifier with the accuracy of 97.78 %.

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Section: Data Set and Methodsmentioning

confidence: 99%

“…This method places the attributes obtained from each data image in the coordinate plane. Then, the classification process is performed by finding the hyper-plane that separates the two classes well [15].…”

Section: Classifiersmentioning

confidence: 99%

Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

Toğaçar¹,

Ergen²,

Sertkaya³

2019

ELEKTRON ELEKTROTECH

View full text Add to dashboard Cite

show abstract

“…The computed curve length < L ( k )> for a different value of k is related to the FD D by the exponential formula < L ( k )>∝ k −D . The FD (noted as FD_Hig) is estimated as the slope of a fitted regression curve to the log-log plot of < L ( k )> versus k [ 10 ].…”

Section: Methodsmentioning

confidence: 99%

“…Doret et al applied a univariate analysis method for the frequency domain and nonlinear parameters [ 9 ]; the former had the higher area under the curve (AUC) value of 0.81 ± 0.07 with a 95% confidence interval (CI). Comert et al proposed a prognostic model, based on image-based time-frequency (IBTF) features and least square SVM (LS-SVM), which achieved better performance than conventional feature-based methods [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State

Zhao

Zhang

Deng

2018

JCM

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Continuous monitoring of the fetal heart rate (FHR) signal has been widely used to allow obstetricians to obtain detailed physiological information about newborns. However, visual interpretation of FHR traces causes inter-observer and intra-observer variability. Therefore, this study proposed a novel computerized analysis software of the FHR signal (CAS-FHR), aimed at providing medical decision support. First, to the best of our knowledge, the software extracted the most comprehensive features (47) from different domains, including morphological, time, and frequency and nonlinear domains. Then, for the intelligent assessment of fetal state, three representative machine learning algorithms (decision tree (DT), support vector machine (SVM), and adaptive boosting (AdaBoost)) were chosen to execute the classification stage. To improve the performance, feature selection/dimensionality reduction methods (statistical test (ST), area under the curve (AUC), and principal component analysis (PCA)) were designed to determine informative features. Finally, the experimental results showed that AdaBoost had stronger classification ability, and the performance of the selected feature set using ST was better than that of the original dataset with accuracies of 92% and 89%, sensitivities of 92% and 89%, specificities of 90% and 88%, and F-measures of 95% and 92%, respectively. In summary, the results proved the effectiveness of our proposed approach involving the comprehensive analysis of the FHR signal for the intelligent prediction of fetal asphyxia accurately in clinical practice.

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“…Figure 4 shows the detection and tracking of the circle under water with the trained model. The most important feature of the CNN model is that it is faster, more efficient in image processing, and most importantly, it automatically extracts attributes and reflects them to the result (Cömert, Kocamaz, & Subha, 2018).…”

Section: Resultsmentioning

confidence: 99%

İnsansız Su Altı Araçlarında Derin Öğrenme Yöntemleri

Ataner

Özdeş

Öztürk

et al. 2020

European Journal of Science and Technology

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Unmanned underwater vehicles (ROV/AUV) are robotic systems that can float underwater, are autonomous and remotely controlled. Nowadays, the Navy has focused on the operational use of unmanned underwater vehicles in the defense industry and in many areas, and has increased interest in this issue. Unmanned underwater vehicles. Unmanned underwater vehicles are carried out in civilian and military applications for different and varied purposes like protection of national sources, protection of environmental sources and researchs about that, miscellaneous construction activities, police of coastal and country. Also they can use civil and military applications and they helped they have helped with much of the academic and industrial research done in recent years. To sum up they are remotely controlled vehicles with observation and exploration features. This article discusses image processing and deep learning techniques in unmanned underwater vehicles. Also it presents an in-depth review of the artificial intelligence technique and aims to contribute to our country's defense industry. The options that will enable the vehicle to succeed in autonomous missions are mentioned. The Raspberry Pi 3 microprocessor was used in autonomous missions. The Raspberry Pi Camera Module, which is compatible with the Raspberry Pi 3, is preferred. Python was used as a programming language during software process. Objects in the images taken from the camera have been identified using the OpenCV library and deep learning. The TensorFlow library which deep learning library, was used for object detection and tracking. At the beginning The Faster-RCNN-Inception-V2 model was used as the Model. However, Faster-RCNN-Inception-V2 model and Raspberry Pi 3 FPS cooperation working did not show a good performance. For this reason, the SSDLite-MobileNet-V2 model, which is fast enough for most real-time object detection applications, is preferred.

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Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment

Cited by 72 publications

References 66 publications

Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

A Comprehensive Feature Analysis of the Fetal Heart Rate Signal for the Intelligent Assessment of Fetal State

İnsansız Su Altı Araçlarında Derin Öğrenme Yöntemleri

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