scholarly journals Automated scoring of pre-REM sleep in mice with deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Niklas Grieger ◽  
Justus T. C. Schwabedal ◽  
Stefanie Wendel ◽  
Yvonne Ritze ◽  
Stephan Bialonski
Keyword(s):  
Deep Learning ◽  
Rem Sleep ◽  
Network Architecture ◽  
Classification Performance ◽  
Sleep Stages ◽  
Data Sets ◽  
Manual Task ◽  
Typical Data ◽  
Out Of Sample ◽  
Simple Neural Network

AbstractReliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.

scholarly journals ECOVNet: a highly effective ensemble based deep learning model for detecting COVID-19

2021 ◽  
Vol 7 ◽  
pp. e551
Author(s):  
Nihad Karim Chowdhury ◽  
Muhammad Ashad Kabir ◽  
Md. Muhtadir Rahman ◽  
Noortaz Rezoana
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Learning Model ◽  
Classification Performance ◽  
Data Sets ◽  
X Rays ◽  
Proposed Model ◽  
Highly Effective ◽  
Chest X Ray ◽  
Deep Learning Model

The goal of this research is to develop and implement a highly effective deep learning model for detecting COVID-19. To achieve this goal, in this paper, we propose an ensemble of Convolutional Neural Network (CNN) based on EfficientNet, named ECOVNet, to detect COVID-19 from chest X-rays. To make the proposed model more robust, we have used one of the largest open-access chest X-ray data sets named COVIDx containing three classes—COVID-19, normal, and pneumonia. For feature extraction, we have applied an effective CNN structure, namely EfficientNet, with ImageNet pre-training weights. The generated features are transferred into custom fine-tuned top layers followed by a set of model snapshots. The predictions of the model snapshots (which are created during a single training) are consolidated through two ensemble strategies, i.e., hard ensemble and soft ensemble, to enhance classification performance. In addition, a visualization technique is incorporated to highlight areas that distinguish classes, thereby enhancing the understanding of primal components related to COVID-19. The results of our empirical evaluations show that the proposed ECOVNet model outperforms the state-of-the-art approaches and significantly improves detection performance with 100% recall for COVID-19 and overall accuracy of 96.07%. We believe that ECOVNet can enhance the detection of COVID-19 disease, and thus, underpin a fully automated and efficacious COVID-19 detection system.

scholarly journals Deep Learning Iris Recognition Method based on Capsule Network Architecture

2020 ◽  
Vol 8 (6) ◽  
pp. 668-671
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Network Architecture ◽  
Iris Recognition ◽  
Routing Algorithm ◽  
Data Sets ◽  
Recognition Method ◽  
Points Of Interest ◽  
Recognition Ability ◽  
Iris Data

In biometric acknowledgment, which is generally utilized in different fields. As of late, numerous profound learning strategies have been utilized in biometric acknowledgment, attributable to their points of interest. In this deep learning process we adjust the existing network structure and providing the modified routing algorithm technique which is depends on dynamic routing between two capsule layers. This layers helps to maintain and adopt a iris recognition. Various iris data sets are used for recognition. These datasets are trained and tested with the help of different pupil size of an iris. In order to show the recognition ability when the environment varies. The test of dataset achieves 96.2%. CASIA-V4 Lamp dataset gives the highest accuracy of 98.34%. It shows the apply of capsule network in iris recognition.

Performance of deep learning models constructed using panoramic radiographs from two hospitals to diagnose fractures of the mandibular condyle

2021 ◽  
pp. 20200611
Author(s):  
Masako Nishiyama ◽  
Kenichiro Ishibashi ◽  
Yoshiko Ariji ◽  
Motoki Fukuda ◽  
Wataru Nishiyama ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Characteristic Curve ◽  
Mandibular Condyle ◽  
Internal Validity ◽  
Classification Performance ◽  
Data Sets ◽  
Panoramic Radiographs ◽  
Significant Difference ◽  
Low Performance

Objective: The present study aimed to verify the classification performance of deep learning (DL) models for diagnosing fractures of the mandibular condyle on panoramic radiographs using data sets from two hospitals and to compare their internal and external validities. Methods: Panoramic radiographs of 100 condyles with and without fractures were collected from two hospitals and a fivefold cross-validation method was employed to construct and evaluate the DL models. The internal and external validities of classification performance were evaluated as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: For internal validity, high classification performance was obtained, with AUC values of >0.85. Conversely, external validity for the data sets from the two hospitals exhibited low performance. Using combined data sets from both hospitals, the DL model exhibited high performance, which was slightly superior or equal to that of the internal validity but without a statistically significant difference. Conclusion: The constructed DL model can be clinically employed for diagnosing fractures of the mandibular condyle using panoramic radiographs. However, the domain shift phenomenon should be considered when generalizing DL systems.

scholarly journals COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Mohd Zulfaezal Che Azemin ◽  
Radhiana Hassan ◽  
Mohd Izzuddin Mohd Tamrin ◽  
Mohd Adli Md Ali
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Clinical Findings ◽  
Training Data ◽  
Receiver Operating Curve ◽  
Data Sets ◽  
Data Set ◽  
X Ray ◽  
Chest X Ray ◽  
Sensitivity Specificity

The key component in deep learning research is the availability of training data sets. With a limited number of publicly available COVID-19 chest X-ray images, the generalization and robustness of deep learning models to detect COVID-19 cases developed based on these images are questionable. We aimed to use thousands of readily available chest radiograph images with clinical findings associated with COVID-19 as a training data set, mutually exclusive from the images with confirmed COVID-19 cases, which will be used as the testing data set. We used a deep learning model based on the ResNet-101 convolutional neural network architecture, which was pretrained to recognize objects from a million of images and then retrained to detect abnormality in chest X-ray images. The performance of the model in terms of area under the receiver operating curve, sensitivity, specificity, and accuracy was 0.82, 77.3%, 71.8%, and 71.9%, respectively. The strength of this study lies in the use of labels that have a strong clinical association with COVID-19 cases and the use of mutually exclusive publicly available data for training, validation, and testing.

scholarly journals Transparent Classification with Multilayer Logical Perceptrons and Random Binarization

2020 ◽  
Vol 34 (04) ◽  
pp. 6331-6339
Author(s):  
Zhuo Wang ◽  
Wei Zhang ◽  
Ning LIU ◽  
Jianyong Wang
Keyword(s):  
Network Architecture ◽  
Gradient Descent ◽  
Classification Performance ◽  
Data Sets ◽  
Neural Network Architecture ◽  
Continuous Version ◽  
Continuous Space ◽  
Public Data ◽  
Rule Sets ◽  
Classification Tasks

Models with transparent inner structure and high classification performance are required to reduce potential risk and provide trust for users in domains like health care, finance, security, etc. However, existing models are hard to simultaneously satisfy the above two properties. In this paper, we propose a new hierarchical rule-based model for classification tasks, named Concept Rule Sets (CRS), which has both a strong expressive ability and a transparent inner structure. To address the challenge of efficiently learning the non-differentiable CRS model, we propose a novel neural network architecture, Multilayer Logical Perceptron (MLLP), which is a continuous version of CRS. Using MLLP and the Random Binarization (RB) method we proposed, we can search the discrete solution of CRS in continuous space using gradient descent and ensure the discrete CRS acts almost the same as the corresponding continuous MLLP. Experiments on 12 public data sets show that CRS outperforms the state-of-the-art approaches and the complexity of the learned CRS is close to the simple decision tree.

scholarly journals P137 Deep learning enables accurate automatic sleep stage classification in a clinical paediatric population

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A66-A66
Author(s):  
P Somaskandhan ◽  
H Korkalainen ◽  
P Terrill ◽  
S Sigurðardóttir ◽  
E Arnardóttir ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sleep Disorders ◽  
Network Architecture ◽  
Short Term Memory ◽  
Sleep Stage ◽  
Paediatric Population ◽  
Classification Model ◽  
Sleep Stages ◽  
Automatic Method ◽  
Accurate Identification

Abstract Introduction Sleep disorders are widespread in children and associated with a myriad of detrimental health sequelae. Accurate identification of sleep stages is crucial in diagnosing various sleep disorders; however, manual sleep stage scoring can be subjective, laborious, and costly. To tackle these shortcomings, we aimed to develop an accurate deep learning-based approach to automate sleep staging in a paediatric cohort. Methods A clinical dataset (n=115, 35% girls) containing overnight polysomnographic recordings of 10–13-year-old Icelandic children from the EuroPrevall-iFAAM study was utilised to develop a combined convolutional and long short-term memory neural network architecture. A three-channel input comprising electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography was used to train and evaluate the model to classify sleep into five stages (wake/N1/N2/N3/REM) using 10-fold cross-validation. Further, inter-rater reliabilities between two manual scorers and the automatic method were investigated in a subset (n=10) of the population. Results The automatic classification model achieved an accuracy of 84.5% (Cohen’s kappa κ=0.78: substantial agreement with manual scorings). Inter-rater reliability attained between two manual scorers was 84.6% (κ=0.78), and the automatic method achieved similar concordances with them, 83.4% (κ=0.76) and 82.7% (κ=0.75). Discussion The developed model achieved high accuracy and compared favourably to previously published state-of-the-art methods (performance range: 74.8%-84.3%). Inter-rater reliabilities were on par with the consensus between manual scorers and even better than among international sleep centres (commonly 0.57–0.63 as per literature). Therefore, incorporating the proposed methodology in clinical practice could be highly beneficial as it enables fast, cost-effective, and accurate sleep classification in children.

scholarly journals Supervised and unsupervised machine learning for automated scoring of sleep–wake and cataplexy in a mouse model of narcolepsy

SLEEP ◽  
2019 ◽  
Vol 43 (5) ◽  
Author(s):  
Ioannis Exarchos ◽  
Anna A Rogers ◽  
Lauren M Aiani ◽  
Robert E Gross ◽  
Gari D Clifford ◽  
...  
Keyword(s):  
Machine Learning ◽  
Rem Sleep ◽  
Network Architecture ◽  
Training Data ◽  
Series Data ◽  
Sleep Stages ◽  
Automated Scoring ◽  
Wild Type ◽  
Unseen Data ◽  
Unsupervised Approach

Abstract Despite commercial availability of software to facilitate sleep–wake scoring of electroencephalography (EEG) and electromyography (EMG) in animals, automated scoring of rodent models of abnormal sleep, such as narcolepsy with cataplexy, has remained elusive. We optimize two machine-learning approaches, supervised and unsupervised, for automated scoring of behavioral states in orexin/ataxin-3 transgenic mice, a validated model of narcolepsy type 1, and additionally test them on wild-type mice. The supervised learning approach uses previously labeled data to facilitate training of a classifier for sleep states, whereas the unsupervised approach aims to discover latent structure and similarities in unlabeled data from which sleep stages are inferred. For the supervised approach, we employ a deep convolutional neural network architecture that is trained on expert-labeled segments of wake, non-REM sleep, and REM sleep in EEG/EMG time series data. The resulting trained classifier is then used to infer on the labels of previously unseen data. For the unsupervised approach, we leverage data dimensionality reduction and clustering techniques. Both approaches successfully score EEG/EMG data, achieving mean accuracies of 95% and 91%, respectively, in narcoleptic mice, and accuracies of 93% and 89%, respectively, in wild-type mice. Notably, the supervised approach generalized well on previously unseen data from the same animals on which it was trained but exhibited lower performance on animals not present in the training data due to inter-subject variability. Cataplexy is scored with a sensitivity of 85% and 57% using the supervised and unsupervised approaches, respectively, when compared to manual scoring, and the specificity exceeds 99% in both cases.

scholarly journals An electrophysiological marker of arousal level in humans

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Janna D Lendner ◽  
Randolph F Helfrich ◽  
Bryce A Mander ◽  
Luis Romundstad ◽  
Jack J Lin ◽  
...  
Keyword(s):  
Eye Movement ◽  
Rem Sleep ◽  
Oscillatory Activity ◽  
Sleep Stages ◽  
Brain State ◽  
Data Sets ◽  
Rapid Eye Movement ◽  
Spectral Slope ◽  
Scale Free ◽  
Eeg Recordings

Deep non-rapid eye movement sleep (NREM) and general anesthesia with propofol are prominent states of reduced arousal linked to the occurrence of synchronized oscillations in the electroencephalogram (EEG). Although rapid eye movement (REM) sleep is also associated with diminished arousal levels, it is characterized by a desynchronized, ‘wake-like’ EEG. This observation implies that reduced arousal states are not necessarily only defined by synchronous oscillatory activity. Using intracranial and surface EEG recordings in four independent data sets, we demonstrate that the 1/f spectral slope of the electrophysiological power spectrum, which reflects the non-oscillatory, scale-free component of neural activity, delineates wakefulness from propofol anesthesia, NREM and REM sleep. Critically, the spectral slope discriminates wakefulness from REM sleep solely based on the neurophysiological brain state. Taken together, our findings describe a common electrophysiological marker that tracks states of reduced arousal, including different sleep stages as well as anesthesia in humans.

Breast Cancer Recognition Algorithm Based on Convolution Neural Network

2021 ◽  
Vol 11 (3) ◽  
pp. 672-680
Author(s):  
Jiafu Jiang ◽  
Xinpei Li ◽  
Jin Wang ◽  
Se-Jung Lim
Keyword(s):  
Fuzzy Logic ◽  
Deep Learning ◽  
Network Architecture ◽  
Medical Images ◽  
Recognition Algorithm ◽  
Natural Images ◽  
Data Sets ◽  
Medical Disputes ◽  
Classification Prediction ◽  
Deep Learning Model

Most of the preliminary attempts of deep learning in medical images focus on replacing natural images with medical images into convolutional neural networks. In doing so, however, the particularity of medical images and the basic differences between the two types of images are ignored. This difference makes it impossible to directly use the network architecture developed for natural images. This paper therefore uses medical data sets for migration learning. Moreover, the reason why deep learning is difficult to apply in medicine is that it can easily lead to medical disputes because of its unexplainability. In this paper, the deep learning model is explained and implemented by using the theory of fuzzy logic. This paper tests the accuracy and stability of the original model and the new model in classification prediction. Our results show that the model implemented by fuzzy logic improves the accuracy, and makes the prediction more stable as well.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

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