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General General

Comparison and Validation of Deep Learning Models for the Diagnosis of Pneumonia.

In Computational intelligence and neuroscience

As a respiratory infection, pneumonia has gained great attention from countries all over the world for its strong spreading and relatively high mortality. For pneumonia, early detection and treatment will reduce its mortality rate significantly. Currently, X-ray diagnosis is recognized as a relatively effective method. The visual analysis of a patient's X-ray chest radiograph by an experienced doctor takes about 5 to 15 minutes. When cases are concentrated, this will undoubtedly put tremendous pressure on the doctor's clinical diagnosis. Therefore, relying on the naked eye of the imaging doctor has very low efficiency. Hence, the use of artificial intelligence for clinical image diagnosis of pneumonia is a necessary thing. In addition, artificial intelligence recognition is very fast, and the convolutional neural networks (CNNs) have achieved better performance than human beings in terms of image identification. Therefore, we used the dataset which has chest X-ray images for classification made available by Kaggle with a total of 5216 train and 624 test images, with 2 classes as normal and pneumonia. We performed studies using five mainstream network algorithms to classify these diseases in the dataset and compared the results, from which we improved MobileNet's network structure and achieved a higher accuracy rate than other methods. Furthermore, the improved MobileNet's network could also extend to other areas for application.

Yue Zhenjia, Ma Liangping, Zhang Runfeng

2020

General General

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities.

In Computational intelligence and neuroscience

Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful "emotional" interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.

Suhaimi Nazmi Sofian, Mountstephens James, Teo Jason

2020

General General

Pseudo2GO: A Graph-Based Deep Learning Method for Pseudogene Function Prediction by Borrowing Information From Coding Genes.

In Frontiers in genetics ; h5-index 62.0

Pseudogenes are indicating more and more functional potentials recently, though historically were regarded as relics of evolution. Computational methods for predicting pseudogene functions on Gene Ontology is important for directing experimental discovery. However, no pseudogene-specific computational methods have been proposed to directly predict their Gene Ontology (GO) terms. The biggest challenge for pseudogene function prediction is the lack of enough features and functional annotations, making training a predictive model difficult. Considering the close functional similarity between pseudogenes and their parent coding genes that share great amount of DNA sequence, as well as that coding genes have rich annotations, we aim to predict pseudogene functions by borrowing information from coding genes in a graph-based way. Here we propose Pseudo2GO, a graph-based deep learning semi-supervised model for pseudogene function prediction. A sequence similarity graph is first constructed to connect pseudogenes and coding genes. Multiple features are incorporated into the model as the node attributes to enable the graph an attributed graph, including expression profiles, interactions with microRNAs, protein-protein interactions (PPIs), and genetic interactions. Graph convolutional networks are used to propagate node attributes across the graph to make classifications on pseudogenes. Comparing Pseudo2GO with other frameworks adapted from popular protein function prediction methods, we demonstrated that our method has achieved state-of-the-art performance, significantly outperforming other methods in terms of the M-AUPR metric.

Fan Kunjie, Zhang Yan

2020

deep learning, feature propagation, function prediction, gene ontology, graph neural networks, pseudogene, semi-supervised learning

General General

Wheat Stripe Rust Grading by Deep Learning With Attention Mechanism and Images From Mobile Devices.

In Frontiers in plant science

Wheat stripe rust is one of the main wheat diseases worldwide, which has significantly adverse effects on wheat yield and quality, posing serious threats on food security. Disease severity grading plays a paramount role in stripe rust disease management including breeding disease-resistant wheat varieties. Manual inspection is time-consuming, labor-intensive and prone to human errors, therefore, there is a clearly urgent need to develop more effective and efficient disease grading strategy by using automated approaches. However, the differences between wheat leaves of different levels of stripe rust infection are usually tiny and subtle, and, as a result, ordinary deep learning networks fail to achieve satisfying performance. By formulating this challenge as a fine-grained image classification problem, this study proposes a novel deep learning network C-DenseNet which embeds Convolutional Block Attention Module (CBAM) in the densely connected convolutional network (DenseNet). The performance of C-DenseNet and its variants is demonstrated via a newly collected wheat stripe rust grading dataset (WSRgrading dataset) at Northwest A&F University, Shaanxi Province, China, which contains a total of 5,242 wheat leaf images with 6 levels of stripe rust infection. The dataset was collected by using various mobile devices in the natural field condition. Comparative experiments show that C-DenseNet with a test accuracy of 97.99% outperforms the classical DenseNet (92.53%) and ResNet (73.43%). GradCAM++ network visualization also shows that C-DenseNet is able to pay more attention to the key areas in making the decision. It is concluded that C-DenseNet with an attention mechanism is suitable for wheat stripe rust disease grading in field conditions.

Mi Zhiwen, Zhang Xudong, Su Jinya, Han Dejun, Su Baofeng

2020

C-DenseNet, CBAM module, attention mechanism, disease grading, wheat stripe rust

Ophthalmology Ophthalmology

Predicting Glaucoma before Onset Using Deep Learning.

In Ophthalmology. Glaucoma

PURPOSE : To assess the accuracy of deep learning models to predict glaucoma development from fundus photographs several years before disease onset.

DESIGN : Algorithm development for predicting glaucoma using data from a prospective longitudinal study.

PARTICIPANTS : A total of 66 721 fundus photographs from 3272 eyes of 1636 subjects who participated in the Ocular Hypertension Treatment Study (OHTS) were included.

MAIN OUTCOME MEASURES : Accuracy and area under the curve (AUC).

METHODS : Fundus photographs and visual fields were carefully examined by 2 independent readers from the optic disc and visual field reading centers of the OHTS. When an abnormality was detected by the readers, the subject was recalled for retesting to confirm the abnormality and for further confirmation by an end point committee. By using 66 721 fundus photographs, deep learning models were trained and validated using 85% of the fundus photographs and further retested (validated) on the remaining (held-out) 15% of the fundus photographs.

RESULTS : The AUC of the deep learning model in predicting glaucoma development 4 to 7 years before disease onset was 0.77 (95% confidence interval [CI], 0.75-0.79). The accuracy of the model in predicting glaucoma development approximately 1 to 3 years before disease onset was 0.88 (95% CI, 0.86-0.91). The accuracy of the model in detecting glaucoma after onset was 0.95 (95% CI, 0.94-0.96).

CONCLUSIONS : Deep learning models can predict glaucoma development before disease onset with reasonable accuracy. Eyes with visual field abnormality but not glaucomatous optic neuropathy had a higher tendency to be missed by deep learning algorithms.

Thakur Anshul, Goldbaum Michael, Yousefi Siamak

Internal Medicine Internal Medicine

Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation.

In Annals of emergency medicine ; h5-index 53.0

STUDY OBJECTIVE : The goal of this study is to create a predictive, interpretable model of early hospital respiratory failure among emergency department (ED) patients admitted with coronavirus disease 2019 (COVID-19).

METHODS : This was an observational, retrospective, cohort study from a 9-ED health system of admitted adult patients with severe acute respiratory syndrome coronavirus 2 (COVID-19) and an oxygen requirement less than or equal to 6 L/min. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of greater than 10 L/min by low-flow device, high-flow device, noninvasive or invasive ventilation, or death. Predictive models were compared with the Elixhauser Comorbidity Index, quick Sequential [Sepsis-related] Organ Failure Assessment, and the CURB-65 pneumonia severity score.

RESULTS : During the study period, from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. On the independent test cohort, both a novel bedside scoring system, the quick COVID-19 Severity Index (area under receiver operating characteristic curve mean 0.81 [95% confidence interval {CI} 0.73 to 0.89]), and a machine-learning model, the COVID-19 Severity Index (mean 0.76 [95% CI 0.65 to 0.86]), outperformed the Elixhauser mortality index (mean 0.61 [95% CI 0.51 to 0.70]), CURB-65 (0.50 [95% CI 0.40 to 0.60]), and quick Sequential [Sepsis-related] Organ Failure Assessment (0.59 [95% CI 0.50 to 0.68]). A low quick COVID-19 Severity Index score was associated with a less than 5% risk of respiratory decompensation in the validation cohort.

CONCLUSION : A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted with bedside respiratory examination findings within a simple scoring system.

Haimovich Adrian D, Ravindra Neal G, Stoytchev Stoytcho, Young H Patrick, Wilson Francis P, van Dijk David, Schulz Wade L, Taylor R Andrew

2020-Oct