Transfer learning to detect neonatal seizure from electroencephalography signals

Çalışkan, Abdullah; Rencuzogullari, Suleyman

doi:10.1007/s00521-021-05878-y

Cited by 18 publications

(10 citation statements)

References 43 publications

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“…Similar to normal cells, the number of misdetected cells is reduced and accuracy is improved [ 13 ]. It is verified on the ICPR 2016 and 2018 datasets that this cascade detection method can achieve better detection results [ 14 ]. Mitosis is characterized by the presence of spindles and chromosomes in the process of cell division, so that the daughter chromosomes copied in S phase are evenly distributed to the daughter cells.…”

Section: Application Methods Of Artificial Intelligence-based Semisup...mentioning

confidence: 99%

Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation

Liu

2022

Computational Intelligence and Neuroscience

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In the field of medical image processing, due to the differences in tissues, organs, and imaging methods, obtained medical images have significant differences. With the development of intelligence in medicine, an increasing number of computing optimization algorithms based on AI technology have also been applied to the field of medicine. Because the image segmentation algorithm based on the semisupervised self-training algorithm solves initialization class center large randomness problem in the traditional cluster-based image segmentation algorithm, this article aims to integrate the artificial intelligence semisupervised self-training algorithm into the pathological tissue image segmentation problem. An experimental group is designed to collect sample images and the algorithm proposed in this article is used to perform image segmentation to achieve a better visual experience and images. Although there is no general image segmentation theory, many scholars have been committed to applying new concepts and new methods to image segmentation in recent years and combining specific theoretical image segmentation methods has achieved good application results in image segmentation. For example, wavelet analysis, wavelet transform, neural networks, and genetic algorithms can effectively improve the segmentation effect. The results of the Seg cutting method designed in this article show that, in retinal blood vessel segmentation results on a database of healthy people, the sensitivity value is 0.941633, the false-positive rate is 0.952933, the specificity is 0.956787, and the accuracy rate is 0.96182, which are all higher than those in other methods. Image cutting methods such as FNN, CNN, and AWN have addressed the case tissue image cutting problem. Using the Seg cutting method designed in this article to segment the retinal blood vessels on a diabetes patient database, the sensitivity value is 0.8106, the false-positive rate is 0.0511, the specificity is 0.9712, the accuracy is 0.9421, and the false-positive rate is omitted. The false-positive rate is lower than AWN, and other indicators are higher than FNN, CNN, AWN, and other image cutting methods. The application of artificial intelligence-based semisupervised self-training algorithms in pathological tissue image segmentation is realized.

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Section: Application Methods Of Artificial Intelligence-based Semisup...mentioning

confidence: 99%

Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation

Liu

2022

Computational Intelligence and Neuroscience

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“…In the CNN method, several parameters may need to be regulated in the architectures we have designed, and this process can be time-consuming. For this reason, in some studies, well-designed CNN architectures such as AlexNet and DenseNet are preferred at the classification stage [32]. Information in a CNN-processed raw image is preserved.…”

Section: Convolutional Neural Network and The Proposed Dcnn Modelmentioning

confidence: 99%

“…Information in a CNN-processed raw image is preserved. In an image applied as an input to the CNN model, the information between the pixels is included in the networks [30,32]. There are many parameters that need to be adjusted when designing the CNN model.…”

Section: Convolutional Neural Network and The Proposed Dcnn Modelmentioning

confidence: 99%

“…It is the layer responsible for producing the output of each neuron in the input layer. The final output of the convolution layer is a vector [29,32,33]. ii.…”

Section: Imentioning

confidence: 99%

“…The pooling layer can protect the network by subsampling the output of the convolution layer. By reducing the amount of parameters and calculations in the network, mismatch in the network is controlled and overfitting can be avoided [28,32,33]. iii.…”

Section: Imentioning

confidence: 99%

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A New Hybrid Approach Based on Time Frequency Images and Deep Learning Methods for Diagnosis of Migraine Disease and Investigation of Stimulus Effect

2023

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Migraine is a neurological disorder that is associated with severe headaches and seriously affects the lives of patients. Diagnosing Migraine Disease (MD) can be laborious and time-consuming for specialists. For this reason, systems that can assist specialists in the early diagnosis of MD are important. Although migraine is one of the most common neurological diseases, there are very few studies on the diagnosis of MD, especially electroencephalogram (EEG)-and deep learning (DL)-based studies. For this reason, in this study, a new system has been proposed for the early diagnosis of EEG- and DL-based MD. In the proposed study, EEG signals obtained from the resting state (R), visual stimulus (V), and auditory stimulus (A) from 18 migraine patients and 21 healthy control (HC) groups were used. By applying continuous wavelet transform (CWT) and short-time Fourier transform (STFT) methods to these EEG signals, scalogram-spectrogram images were obtained in the time-frequency (T-F) plane. Then, these images were applied as inputs in three different convolutional neural networks (CNN) architectures (AlexNet, ResNet50, SqueezeNet) that proposed deep convolutional neural network (DCNN) models and classification was performed. The results of the classification process were evaluated, taking into account accuracy (acc.), sensitivity (sens.), specificity (spec.), and performance criteria, and the performances of the preferred methods and models in this study were compared. In this way, the situation, method, and model that showed the most successful performance for the early diagnosis of MD were determined. Although the classification results are close to each other, the resting state, CWT method, and AlexNet classifier showed the most successful performance (Acc: 99.74%, Sens: 99.9%, Spec: 99.52%). We think that the results obtained in this study are promising for the early diagnosis of MD and can be of help to experts.

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A Comparative Study on EEG Features for Neonatal Seizure Detection

Abirami

Thomas

Yuvaraj

et al. 2022

Biomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders

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Transfer learning to detect neonatal seizure from electroencephalography signals

Cited by 18 publications

References 43 publications

Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation

Artificial Intelligence-Based Semisupervised Self-Training Algorithm in Pathological Tissue Image Segmentation

A New Hybrid Approach Based on Time Frequency Images and Deep Learning Methods for Diagnosis of Migraine Disease and Investigation of Stimulus Effect

A Comparative Study on EEG Features for Neonatal Seizure Detection

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