Identification of Complex Mixtures for Raman Spectroscopy Using a Novel Scheme Based on a New Multi-Label Deep Neural Network

Pan, Liangrui; Pipitsunthonsan, Pronthep; Daengngam, Chalongrat; Channumsin, Sittiporn; Sreesawet, Suwat; Chongcheawchamnan, M.

doi:10.1109/jsen.2021.3059849

Cited by 19 publications

(14 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the model, we need to perform a detailed analysis of each category. Therefore, we need to count true positive (TP), false positive (FP), true negative (TN), and false positive (FN) [ 33 ]. Test accuracy: the proportion of samples correctly predicted to the total samples Precision: the ratio of true positive predictions to total positive predictions Recall: Ratio of true positive to the total observation made by the proposed model F1 Score: It is the harmonic mean of precision and recall Confusion matrix: It is the measurement of the performance of the model.…”

Section: Resultsmentioning

confidence: 99%

MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images

Pan

Wang

et al. 2022

Health Inf Sci Syst

Self Cite

View full text Add to dashboard Cite

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID19) is life-saving important for both patients and doctors. This research proposes a multi-channel feature deep neural network (MFDNN) algorithm to screen people infected with COVID19. The algorithm integrates data over-sampling technology and MFDNN model to carry out the training. The oversampling technique reduces the deviation of the prior probability of the MFDNN algorithm on unbalanced data. Multi-channel feature fusion technology improves the efficiency of feature extraction and the accuracy of model diagnosis. In the experiment, Compared with traditional deep learning models (VGG19, GoogLeNet, Resnet50, Desnet201), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Furthermore, through ablation experiments, we proved that a multi-channel convolutional neural network (CNN) is superior to single-channel CNN, additional layer and PSN module, and indirectly proved the sufficiency and necessity of each step of the MFDNN classification method. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19 .

show abstract

Section: Resultsmentioning

confidence: 99%

MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images

Pan

Wang

et al. 2022

Health Inf Sci Syst

Self Cite

View full text Add to dashboard Cite

show abstract

“…To evaluate the model, we need to perform a detailed analysis of each category. Therefore, we need to count true positives, false positives, true negatives, and false positives [26].…”

Section: Resultsmentioning

confidence: 99%

MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

Pan

Wang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

show abstract

“…By observing the results in Figures 7 and 8, the 2-layer unidirectional LSTM has 768,000 more parameters than the 1-layer bidirectional LSTM, and the accuracy rate is also reduced by 0.4%. Therefore, the performance of the 1-layer bidirectional LSTM is used to compare with others networks [34,35]. layer with bidirectional and 2 layers with unidirectional architectures have relatively high accuracy outcomes.…”

Section: Type 4 Bidirectional Multi-layersmentioning

confidence: 99%

“…The result of Section 3.1 is based on the model of CNN in the proposed CCLN using [15]. In order to consider the utility of using the general network architecture, this study evaluates several general CNN models, including VGG16, VGG19, ResNet50, DenseNet121 and MobileNet [34,35]. Evaluating the impact of transfer learning on overall performance is as following.…”

Section: The Comparison Of Various Modelsmentioning

confidence: 99%

Gaze Tracking Based on Concatenating Spatial-Temporal Features

Hwang

Chen

Hsieh

et al. 2022

Sensors

View full text Add to dashboard Cite

Based on experimental observations, there is a correlation between time and consecutive gaze positions in visual behaviors. Previous studies on gaze point estimation usually use images as the input for model trainings without taking into account the sequence relationship between image data. In addition to the spatial features, the temporal features are considered to improve the accuracy in this paper by using videos instead of images as the input data. To be able to capture spatial and temporal features at the same time, the convolutional neural network (CNN) and long short-term memory (LSTM) network are introduced to build a training model. In this way, CNN is used to extract the spatial features, and LSTM correlates temporal features. This paper presents a CNN Concatenating LSTM network (CCLN) that concatenates spatial and temporal features to improve the performance of gaze estimation in the case of time-series videos as the input training data. In addition, the proposed model can be optimized by exploring the numbers of LSTM layers, the influence of batch normalization (BN) and global average pooling layer (GAP) on CCLN. It is generally believed that larger amounts of training data will lead to better models. To provide data for training and prediction, we propose a method for constructing datasets of video for gaze point estimation. The issues are studied, including the effectiveness of different commonly used general models and the impact of transfer learning. Through exhaustive evaluation, it has been proved that the proposed method achieves a better prediction accuracy than the existing CNN-based methods. Finally, 93.1% of the best model and 92.6% of the general model MobileNet are obtained.

show abstract

Identification of Complex Mixtures for Raman Spectroscopy Using a Novel Scheme Based on a New Multi-Label Deep Neural Network

Cited by 19 publications

References 39 publications

MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images

MFDNN: multi-channel feature deep neural network algorithm to identify COVID19 chest X-ray images

MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

Gaze Tracking Based on Concatenating Spatial-Temporal Features

Contact Info

Product

Resources

About