A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images

Ieracitano, Cosimo; Mammone, Nadia; Versaci, Mario; Varone, Giuseppe; Ali, Abder-Rahman; Armentano, A.; Calabrese, Grazia; Ferrarelli, Anna; Turano, L; Tebala, Carmela; Hussain, Zain; Sheikh, Zakariya; Sheikh, Aziz; Sceni, Giuseppe; Hussain, Amir; Morabito, F.

doi:10.1016/j.neucom.2022.01.055

Cited by 113 publications

(50 citation statements)

References 47 publications

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“…Automated detection methods have been developed to address the issues with the huge amount of data and manual analysis. Deep-learningbased methods hold great importance in detection tasks and perform successfully in various domains [12,[16][17][18]. The proven performance of deep learning techniques directed researchers to study deep neural networks (DNNs) in respect to the animal detection task.…”

Section: Related Workmentioning

confidence: 99%

Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN

Pavlovs

Aktas

Avots

et al. 2022

Entropy

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Changes in the ungulate population density in the wild has impacts on both the wildlife and human society. In order to control the ungulate population movement, monitoring systems such as camera trap networks have been implemented in a non-invasive setup. However, such systems produce a large number of images as the output, hence making it very resource consuming to manually detect the animals. In this paper, we present a new dataset of wild ungulates which was collected in Latvia. Moreover, we demonstrate two methods, which use RetinaNet and Faster R-CNN as backbones, respectively, to detect the animals in the images. We discuss the optimization of training and impact of data augmentation on the performance. Finally, we show the result of aforementioned tune networks over the real world data collected in Latvia.

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Section: Related Workmentioning

confidence: 99%

Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN

Pavlovs

Aktas

Avots

et al. 2022

Entropy

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“…The methods of segmentation first and then classification cannot achieve end-to-end application. In addition, there are some methods based on feature extraction [41] , [42] , which are usually used to manually extract image features, such as texture, and then neural networks are used for classification. This method increases manual operations, and the results are no better than those obtained using neural networks directly.…”

Section: Related Workmentioning

confidence: 99%

Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images

Liu

Qi²,

Chen³

et al. 2022

Computers in Biology and Medicine

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“…In a study, researchers developed an enhanced fuzzy-based deep learning model to differentiate between COVID-19 and infectious pneumonia (no-COVID-19) based on portable CXRs and achieved up to 81% accuracy. eir fuzzy model had only three misclassifications on the validation dataset [24].…”

Section: Introductionmentioning

confidence: 97%

“…To date, several studies have been published on the application of machine learning to develop diagnostic models or predict the death of patients due to COVID-19 [14][15][16][17][18][19][20][21][22][23]. For example, several deep learning models have been reported to diagnose COVID-19 based on images [24]. In a study, researchers developed an enhanced fuzzy-based deep learning model to differentiate between COVID-19 and infectious pneumonia (no-COVID-19) based on portable CXRs and achieved up to 81% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations

Zarei

Jamshidnezhad

Shoushtari

et al. 2022

Journal of Healthcare Engineering

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Prediction of the death among COVID-19 patients can help healthcare providers manage the patients better. We aimed to develop machine learning models to predict in-hospital death among these patients. We developed different models using different feature sets and datasets developed using the data balancing method. We used demographic and clinical data from a multicenter COVID-19 registry. We extracted 10,657 records for confirmed patients with PCR or CT scans, who were hospitalized at least for 24 hours at the end of March 2021. The death rate was 16.06%. Generally, models with 60 and 40 features performed better. Among the 240 models, the C5 models with 60 and 40 features performed well. The C5 model with 60 features outperformed the rest based on all evaluation metrics; however, in external validation, C5 with 32 features performed better. This model had high accuracy (91.18%), F-score (0.916), Area under the Curve (0.96), sensitivity (94.2%), and specificity (88%). The model suggested in this study uses simple and available data and can be applied to predict death among COVID-19 patients. Furthermore, we concluded that machine learning models may perform differently in different subpopulations in terms of gender and age groups.

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A fuzzy-enhanced deep learning approach for early detection of Covid-19 pneumonia from portable chest X-ray images

Cited by 113 publications

References 47 publications

Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN

Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN

Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images

Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations

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