scholarly journals Intelligent Optical Microresonator Imaging Sensor for Early Stage Classification of Dynamical Variations

2021 ◽  
Vol 2 (12) ◽  
pp. 2170040
Author(s):  
Anton V. Saetchnikov ◽  
Elina A. Tcherniavskaia ◽  
Vladimir A. Saetchnikov ◽  
Andreas Ostendorf
Keyword(s):  
Early Stage ◽  
Stage Classification ◽  
Imaging Sensor

Compact and Low-Cost Fluorescence Based Flow-Through Analyzer for Early-Stage Classification of Potentially Toxic Algae and in Situ Semiquantification

2018 ◽  
Vol 52 (13) ◽  
pp. 7399-7408 ◽  
Author(s):  
Silvia E. Zieger ◽  
Günter Mistlberger ◽  
Lukas Troi ◽  
Alexander Lang ◽  
Fabio Confalonieri ◽  
...  
Keyword(s):  
Early Stage ◽  
Low Cost ◽  
Toxic Algae ◽  
Stage Classification ◽  
Flow Through

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

scholarly journals Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Early Stage ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Lung Region ◽  
Computed Tomography Images ◽  
Feature Pyramid ◽  
Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

scholarly journals Cross-evaluation of social mining for classification of depressed online personas

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alina Trifan ◽  
José Luis Oliveira
Keyword(s):  
Transfer Learning ◽  
Early Stage ◽  
Post Partum ◽  
Evaluation Study ◽  
Continuous Increase ◽  
Digital Phenotyping ◽  
Public Datasets ◽  
The Impact ◽  
Social Mining

Abstract With the continuous increase in the use of social networks, social mining is steadily becoming a powerful component of digital phenotyping. In this paper we explore social mining for the classification of self-diagnosed depressed users of Reddit as social network. We conduct a cross evaluation study based on two public datasets in order to understand the impact of transfer learning when the data source is virtually the same. We further complement these results with an experiment of transfer learning in post-partum depression classification, using a corpus we have collected for the matter. Our findings show that transfer learning in social mining might still be at an early stage in computational research and we thoroughly discuss its implications.

Lack of agreement in experts’ classification of patients with early-stage knee osteoarthritis

2021 ◽  
Vol 29 ◽  
pp. S299-S300
Author(s):  
A. Mahmoudian ◽  
S. Lohmander ◽  
M. Englund ◽  
P. Hansen ◽  
F. Luyten
Keyword(s):  
Knee Osteoarthritis ◽  
Early Stage

A novel two-stage method of plant seedlings classification based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Tianhong Dai ◽  
Shijie Cong ◽  
Jianping Huang ◽  
Yanwen Zhang ◽  
Xinwang Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Technology ◽  
Two Stage ◽  
Second Stage ◽  
Stage Classification ◽  
Different Types ◽  
Two Stages ◽  
Plant Seedlings

In agricultural production, weed removal is an important part of crop cultivation, but inevitably, other plants compete with crops for nutrients. Only by identifying and removing weeds can the quality of the harvest be guaranteed. Therefore, the distinction between weeds and crops is particularly important. Recently, deep learning technology has also been applied to the field of botany, and achieved good results. Convolutional neural networks are widely used in deep learning because of their excellent classification effects. The purpose of this article is to find a new method of plant seedling classification. This method includes two stages: image segmentation and image classification. The first stage is to use the improved U-Net to segment the dataset, and the second stage is to use six classification networks to classify the seedlings of the segmented dataset. The dataset used for the experiment contained 12 different types of plants, namely, 3 crops and 9 weeds. The model was evaluated by the multi-class statistical analysis of accuracy, recall, precision, and F1-score. The results show that the two-stage classification method combining the improved U-Net segmentation network and the classification network was more conducive to the classification of plant seedlings, and the classification accuracy reaches 97.7%.

Using transfer learning for diabetic retinopathy stage classification

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Enas M.F. El Houby
Keyword(s):  
Diabetic Retinopathy ◽  
Transfer Learning ◽  
Grade Level ◽  
Design Methodology ◽  
Content Type ◽  
Stage Classification ◽  
Set Up ◽  
Retinal Fundus ◽  
Automated Methods

PurposeDiabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.Design/methodology/approachIn this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.FindingsBy conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.Originality/valueIn this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

Sign in / Sign up

Export Citation Format

Share Document