SalaciaML: A Deep Learning Approach for Supporting Ocean Data Quality Control

We present a skillful deep learning algorithm for supporting quality control of ocean temperature measurements, which we name SalaciaML according to Salacia the roman goddess of sea waters. Classical attempts to algorithmically support and partly automate the quality control of ocean data profiles are especially helpful for the gross errors in the data. Range filters, spike detection, and data distribution checks remove reliably the outliers and errors in the data, still wrong classifications occur. Various automated quality control procedures have been successfully implemented within the main international and EU marine data infrastructures (WOD, CMEMS, IQuOD, SDN) but their resulting data products are still containing data anomalies, bad data flagged as good and vice-versa. They also include visual inspection of suspicious measurements, which is a time consuming activity, especially if the number of suspicious data detected is large. A deep learning approach could highly improve our capabilities to quality assess big data collections and contemporary reducing the human effort. Our algorithm SalaciaML is meant to complement classical automated quality control procedures in supporting the time consuming visually inspection of data anomalies by quality control experts. As a first approach we applied the algorithm to a large dataset from the Mediterranean Sea. SalaciaML has been able to detect correctly more than 90% of all good and/or bad data in 11 out of 16 Mediterranean regions.

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Deep learning algorithm helps to standardise ATS/ERS spirometric acceptability and usability criteria

European Respiratory Journal ◽

10.1183/13993003.00603-2020 ◽

2020 ◽

pp. 2000603 ◽

Cited By ~ 1

Author(s):

Nilakash Das ◽

Kenneth Verstraete ◽

Sanja Stanojevic ◽

Marko Topalovic ◽

Jean-Marie Aerts ◽

...

Keyword(s):

Quality Control ◽

Deep Learning ◽

Learning Algorithm ◽

High Sensitivity ◽

Current Approach ◽

Deep Learning Algorithm ◽

Volume Curve ◽

Flow Volume Curve ◽

Shapley Values ◽

Quality Control Criteria

RationaleWhile ATS/ERS quality control criteria for spirometry include several quantitative limits, it also requires manual visual inspection. The current approach is time consuming and leads to high inter-technician variability. We propose a deep learning approach called convolutional neural network (CNN), to standardise spirometric manoeuvre acceptability and usability.Methods and methodsIn 36 873 curves from the national health and nutritional examination survey (NHANES) USA 2011–12, technicians labelled 54% of curves as meeting ATS/ERS 2005 acceptability criteria with satisfactory start and end of test but identified 93% of curves with a usable FEV1. We processed raw data into images of maximal expiratory flow-volume curve (MEFVC), calculated ATS/ERS quantifiable criteria, and developed CNNs to determine manoeuvre acceptability and usability on 90% of the curves. The models were tested on the remaining 10% of curves. We calculated Shapley values to interpret the models.ResultsIn the test set (N=3738), CNN showed an accuracy of 87% for acceptability and 92% for usability, with the latter demonstrating a high sensitivity (92%) and specificity (96%). They were significantly superior (p<0.0001) to ATS/ERS quantifiable rule-based models. Shapley interpretation revealed MEFVC<1 s (MEFVC pattern within first second of exhalation) and plateau in volume-time were most important in determining acceptability, while MEFVC<1 s entirely determined usability.ConclusionThe CNNs identified relevant attributes in spirometric curves to standardise ATS/ERS manoeuvre acceptability and usability recommendations, and further provides individual manoeuvre feedback. Our algorithm combines the visual experience of skilled technicians and ATS/ERS quantitative rules in automating the critical phase of spirometry quality control.

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Deep Learning Approach for COVID-19 Diagnosis Using X-Ray Images

10.21203/rs.3.rs-31278/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Muntasir Al-Asfoor

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Normal Subjects ◽

Learning Approach ◽

Computer Aided Detection ◽

X Ray ◽

Deep Learning Algorithm ◽

Chest X Ray ◽

The Times

Abstract During the times of pandemics, faster diagnosis plays a key role in the response efforts to contain the disease as well as reducing its spread. Computer-aided detection would save time and increase the quality of diagnosis in comparison with manual human diagnosis. Artificial Intelligence (AI) through deep learning is considered as a reliable method to design such systems. In this research paper, an AI based diagnosis approach has been suggested to tackle the COVID-19 pandemic. The proposed system employs a deep learning algorithm on chest x-ray images to detect the infected subjects. An enhanced Convolutional Neural Network (CNN) architecture has been designed with 22 layers which is then trained over a chest x-ray dataset. More after, a classification component has been introduced to classify the x-ray images into two categories (Covid-19 and not Covid-19) of infection. The system has been evaluated through a series of observations and experimentation. The experimental results have shown a promising performance in terms of accuracy. The system has diagnosed Covid-19 with accuracy of 95.7% and normal subjects with accuracy of 93.1 while it showed 96.7 accuracy on Pneumonia.

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