Decision and feature level fusion of deep features extracted from public COVID-19 data-sets

İlhan, Hamza Osman; Serbes, Görkem; Aydın, Nizamettin

doi:10.1007/s10489-021-02945-8

Cited by 20 publications

(10 citation statements)

References 101 publications

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“… Accuracy:99.05%, Specificity:99.6%, F1-score:98.59%. 2251 s Ilhan et al [54] Deep Feature Fusion Covid19:125, Pneumonia:500, Normal:500. Accuracy:90.84%, Precision:100%, Recall:97.6%.…”

Section: Experiments and Resultsmentioning

confidence: 99%

PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates

Bhosale

Patnaik

2023

Biomedical Signal Processing and Control

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“… Accuracy:99.05%, Specificity:99.6%, F1-score:98.59%. 2251 s Ilhan et al [54] Deep Feature Fusion Covid19:125, Pneumonia:500, Normal:500. Accuracy:90.84%, Precision:100%, Recall:97.6%.…”

Section: Experiments and Resultsmentioning

confidence: 99%

PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates

Bhosale

Patnaik

2023

Biomedical Signal Processing and Control

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“…The need for computerized analysis for fast and accurate diagnosis comes to the fore during this pandemic. Several works using automatic deep learning algorithms on CT scans [6][7][8][9][10] and machine learning algorithms on cough sounds [11][12][13][14][15][16][17][18][19][20][21][22] are proposed in literature. The works on CT scans [6][7][8][9][10] provide information about the degree of severity of the individual's lung damage.…”

Section: Related Workmentioning

confidence: 99%

“…Several works using automatic deep learning algorithms on CT scans [6][7][8][9][10] and machine learning algorithms on cough sounds [11][12][13][14][15][16][17][18][19][20][21][22] are proposed in literature. The works on CT scans [6][7][8][9][10] provide information about the degree of severity of the individual's lung damage. In a recent survey [23], numerous studies and open source datasets on CT have been examined.…”

Section: Related Workmentioning

confidence: 99%

MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds

Ulukaya

Sarıca

Erdem

et al. 2023

Med Biol Eng Comput

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Coronavirus has an impact on millions of lives and has been added to the important pandemics that continue to affect with its variants. Since it is transmitted through the respiratory tract, it has had significant effects on public health and social relations. Isolating people who are COVID positive can minimize the transmission, therefore several exams are proposed to detect the virus such as reverse transcription-polymerase chain reaction (RT-PCR), chest X-Ray, and computed tomography (CT). However, these methods suffer from either a low detection rate or high radiation dosage, along with being expensive. In this study, deep neural network–based model capable of detecting coronavirus from only coughing sound, which is fast, remotely operable and has no harmful side effects, has been proposed. The proposed multi-branch model takes M el Frequency Cepstral Coefficients (MFCC), S pectrogram, and C hromagram as inputs and is abbreviated as MSCCov19Net . The system is trained on publicly available crowdsourced datasets, and tested on two unseen (used only for testing) clinical and non-clinical datasets. Experimental outcomes represent that the proposed system outperforms the 6 popular deep learning architectures on four datasets by representing a better generalization ability. The proposed system has reached an accuracy of 61.5 % in Virufy and 90.4 % in NoCoCoDa for unseen test datasets. The Architecture of the Proposed MSCCov19Net Model for Covid-19 Detection

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“…Zhang and Chen [31] proposed a view fusion module for human pose estimation, which combines decision-level information from different stages so a more comprehensive estimation could be generated. Ilhan, et al [32] developed a computer-aided diagnosis system for early diagnosis of COVID-19, which fuses deep features from seven convolutional neural networks (CNN) architectures, feeding them to multiple classifiers using a late fusion strategy. Zuo, et al [33] proposed a deep multifusion framework with classifier-based feature synthesis, which can automatically fuse multi-modal medical pictures, to aid in precision diagnosis and surgery planning in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Early prediction of sepsis using double fusion of deep features and handcrafted features

et al. 2023

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Sepsis is a life-threatening medical condition that is characterized by the dysregulated immune system response to infections, having both high morbidity and mortality rates. Early prediction of sepsis is critical to the decrease of mortality. This paper presents a novel early warning model called Double Fusion Sepsis Predictor (DFSP) for sepsis onset. DFSP is a double fusion framework that combines the benefits of early and late fusion strategies. First, a hybrid deep learning model that combines both the convolutional and recurrent neural networks to extract deep features is proposed. Second, deep features and handcrafted features, such as clinical scores, are concatenated to build the joint feature representation (early fusion). Third, several tree-based models based on joint feature representation are developed to generate the risk scores of sepsis onset that are combined with an End-to-End neural network for final sepsis detection (late fusion). To evaluate DFSP, a retrospective study was conducted, which included patients admitted to the ICUs of a hospital in Shanghai China. The results demonstrate that the DFSP outperforms state-of-the-art approaches in early sepsis prediction.

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Decision and feature level fusion of deep features extracted from public COVID-19 data-sets

Cited by 20 publications

References 101 publications

PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates

PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates

MSCCov19Net: multi-branch deep learning model for COVID-19 detection from cough sounds

Early prediction of sepsis using double fusion of deep features and handcrafted features

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