prikaz prve stranice dokumenta Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework
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Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework
Diagnostics, 13 (2023), 11; 1954. https://doi.org/10.3390/diagnostics13111954
Dubey, Arun Kumar; Chabert, Gian Luca; Carriero, Alessandro; Pasche, Alessio; Danna, Pietro S. C.; Agarwal, Sushant; Mohanty, Lopamudra; Nillmani, Lopamudra; Sharma, Neeraj; Yadav, Sarita; Jain, Achin; Kumar, Ashish; Kalra, Mannudeep K.; Sobel, David W.; Laird, John R.; Singh, Inder M.; Singh, Narpinder; Tsoulfas, George; Fouda, Mostafa M.; Alizad, Azra; Kitas, George D.; Khanna, Narendra N.; Viskovic, Klaudija; Kukuljan, Melita; Al-Maini, Mustafa; El-Baz, Ayman; Saba, Luca; Suri, Jasjit S. Više autora...

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Dubey, A. K., Chabert, G. L., Carriero, A., Pasche, A., Danna, P. S. C., Agarwal, S. ... Suri, J. S. (2023). Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework. Diagnostics, 13. (11). doi: 10.3390/diagnostics13111954

Dubey, Arun Kumar, et al. "Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework." Diagnostics, vol. 13, br. 11, 2023. https://doi.org/10.3390/diagnostics13111954

Dubey, Arun Kumar, Gian Luca Chabert, Alessandro Carriero, Alessio Pasche, Pietro S. C. Danna, Sushant Agarwal, Lopamudra Mohanty, et al. "Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework." Diagnostics 13, br. 11 (2023). https://doi.org/10.3390/diagnostics13111954

Dubey, A. K., et al. (2023) 'Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework', Diagnostics, 13(11). doi: 10.3390/diagnostics13111954

Dubey AK, Chabert GL, Carriero A, Pasche A, Danna PSC, Agarwal S, i sur.. Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework. Diagnostics [Internet]. 02.06.2023. [pristupljeno 05.05.2024.];13(11). doi: 10.3390/diagnostics13111954

A. K. Dubey, et al., "Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework", Diagnostics, vol. 13, br. 11, Lipanj 2023. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:184:366471. [Citirano: 05.05.2024.]

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Naslov (engleski)Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework
AutorArun Kumar Dubey
AutorGian Luca Chabert
AutorAlessandro Carriero
AutorAlessio Pasche
AutorPietro S. C. Danna
AutorSushant Agarwal
AutorLopamudra Mohanty
AutorLopamudra Nillmani
AutorNeeraj Sharma
AutorSarita Yadav
AutorAchin Jain
AutorAshish Kumar
AutorMannudeep K. Kalra
AutorDavid W. Sobel
AutorJohn R. Laird
AutorInder M. Singh
AutorNarpinder Singh
AutorGeorge Tsoulfas
AutorMostafa M. Fouda
AutorAzra Alizad
AutorGeorge D. Kitas
AutorNarendra N. Khanna
AutorKlaudija Viskovic
AutorMelita Kukuljan
AutorMustafa Al-Maini
AutorAyman El-Baz
AutorLuca Saba
AutorJasjit S. Suri
Autorova ustanova(Klinički bolnički centar Rijeka)
Autorova ustanovaSveučilište u Rijeci
Medicinski fakultet
(Katedra za radiologiju)
Znanstveno / umjetničko
područje, polje i grana		BIOMEDICINA I ZDRAVSTVO
Kliničke medicinske znanosti
Radiologija
Sažetak (engleski)
Background and motivation: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. Methodology: The system consists of a cascade of quality control, ResNet–UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL’s. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts—Croatia (80 COVID) and Italy (72 COVID and 30 controls)—leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. Results: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. Conclusion: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaDiagnostics
Brojčani podacivol. 13, br. 11, 1954
e-ISSN2075-4418
DOIhttps://doi.org/10.3390/diagnostics13111954
URN:NBNurn:nbn:hr:184:366471
Datum objave publikacije2023-06-02
URL dokumentahttps://www.mdpi.com/2075-4418/13/11/1954
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje 4.0 međunarodna (CC BY 4.0)
Datum i vrijeme pohrane2023-06-19 12:16:58