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Pregled bibliografske jedinice broj: 1226745

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0

Agarwal, Mohit; Agarwal, Sushant; Saba, Luca; Chabert, Gian Luca; Gupta, Suneet; Carriero, Alessandro; Pasche, Alessio; Danna, Pietro; Mehmedovic, Armin; Faa, Gavino et al.
Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0 // Computers in Biology and Medicine, 146 (2022), 34, 34 doi:10.1016/j.compbiomed.2022.105571 (međunarodna recenzija, članak, znanstveni)

CROSBI ID: 1226745 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0

Autori
Agarwal, Mohit ; Agarwal, Sushant ; Saba, Luca ; Chabert, Gian Luca ; Gupta, Suneet ; Carriero, Alessandro ; Pasche, Alessio ; Danna, Pietro ; Mehmedovic, Armin ; Faa, Gavino ; Shrivastava, Saurabh ; Jain, Kanishka ; Jain, Harsh ; Jujaray, Tanay ; Singh, Inder M. ; Turk, Monika ; Chadha, Paramjit S. ; Johri, Amer M. ; Khanna, Narendra N. ; Mavrogeni, Sophie ; Laird, John R. ; Sobel, David W. ; Miner, Martin ; Balestrieri, Antonella ; Sfikakis, Petros P. ; Tsoulfas, George ; Misra, Durga Prasanna ; Agarwal, Vikas ; Kitas, George D. ; Teji, Jagjit S. ; Al-Maini, Mustafa ; Dhanjil, Surinder K. ; Nicolaides, Andrew ; Sharma, Aditya ; Rathore, Vijay ; Fatemi, Mostafa ; Alizad, Azra ; Krishnan, Pudukode R. ; Yadav, Rajanikant R. ; Nagy, Frence ; Kincses, Zsigmond Tamás ; Ruzsa, Zoltan ; Naidu, Subbaram ; Višković, Klaudija ; Kalra, Manudeep K. ; Suri, Jasjit S.

Izvornik
Computers in Biology and Medicine (0010-4825) 146 (2022); 34, 34

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
AI ; COVID-19 ; Deep learning ; Glass ground opacities ; Hounsfield units ; Lung CT ; Lung segmentation ; Pruning

Sažetak
Background: COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization. Method: ology: The proposed study uses multicenter ∼9, 000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted. Results: Pruning algorithms (i) FCN-DE, (ii) FCN- GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet- PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions. Conclusions: Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.

Izvorni jezik
Engleski

Znanstvena područja
Kliničke medicinske znanosti



POVEZANOST RADA

Ustanove:
Klinika za infektivne bolesti "Dr Fran Mihaljević",
Zdravstveno veleučilište, Zagreb,
Fakultet zdravstvenih studija u Rijeci

Profili:

Avatar Url Klaudija Višković (autor)

Poveznice na cjeloviti tekst rada:

doi www.ncbi.nlm.nih.gov

Citiraj ovu publikaciju:

Agarwal, Mohit; Agarwal, Sushant; Saba, Luca; Chabert, Gian Luca; Gupta, Suneet; Carriero, Alessandro; Pasche, Alessio; Danna, Pietro; Mehmedovic, Armin; Faa, Gavino et al.
Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0 // Computers in Biology and Medicine, 146 (2022), 34, 34 doi:10.1016/j.compbiomed.2022.105571 (međunarodna recenzija, članak, znanstveni)
Agarwal, M., Agarwal, S., Saba, L., Chabert, G., Gupta, S., Carriero, A., Pasche, A., Danna, P., Mehmedovic, A. & Faa, G. (2022) Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0. Computers in Biology and Medicine, 146, 34, 34 doi:10.1016/j.compbiomed.2022.105571.
@article{article, author = {Agarwal, Mohit and Agarwal, Sushant and Saba, Luca and Chabert, Gian Luca and Gupta, Suneet and Carriero, Alessandro and Pasche, Alessio and Danna, Pietro and Mehmedovic, Armin and Faa, Gavino and Shrivastava, Saurabh and Jain, Kanishka and Jain, Harsh and Jujaray, Tanay and Singh, Inder M. and Turk, Monika and Chadha, Paramjit S. and Johri, Amer M. and Khanna, Narendra N. and Mavrogeni, Sophie and Laird, John R. and Sobel, David W. and Miner, Martin and Balestrieri, Antonella and Sfikakis, Petros P. and Tsoulfas, George and Misra, Durga Prasanna and Agarwal, Vikas and Kitas, George D. and Teji, Jagjit S. and Al-Maini, Mustafa and Dhanjil, Surinder K. and Nicolaides, Andrew and Sharma, Aditya and Rathore, Vijay and Fatemi, Mostafa and Alizad, Azra and Krishnan, Pudukode R. and Yadav, Rajanikant R. and Nagy, Frence and Kincses, Zsigmond Tam\'{a}s and Ruzsa, Zoltan and Naidu, Subbaram and Vi\v{s}kovi\'{c}, Klaudija and Kalra, Manudeep K. and Suri, Jasjit S.}, year = {2022}, pages = {34}, DOI = {10.1016/j.compbiomed.2022.105571}, chapter = {34}, keywords = {AI, COVID-19, Deep learning, Glass ground opacities, Hounsfield units, Lung CT, Lung segmentation, Pruning}, journal = {Computers in Biology and Medicine}, doi = {10.1016/j.compbiomed.2022.105571}, volume = {146}, issn = {0010-4825}, title = {Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0}, keyword = {AI, COVID-19, Deep learning, Glass ground opacities, Hounsfield units, Lung CT, Lung segmentation, Pruning}, chapternumber = {34} }
@article{article, author = {Agarwal, Mohit and Agarwal, Sushant and Saba, Luca and Chabert, Gian Luca and Gupta, Suneet and Carriero, Alessandro and Pasche, Alessio and Danna, Pietro and Mehmedovic, Armin and Faa, Gavino and Shrivastava, Saurabh and Jain, Kanishka and Jain, Harsh and Jujaray, Tanay and Singh, Inder M. and Turk, Monika and Chadha, Paramjit S. and Johri, Amer M. and Khanna, Narendra N. and Mavrogeni, Sophie and Laird, John R. and Sobel, David W. and Miner, Martin and Balestrieri, Antonella and Sfikakis, Petros P. and Tsoulfas, George and Misra, Durga Prasanna and Agarwal, Vikas and Kitas, George D. and Teji, Jagjit S. and Al-Maini, Mustafa and Dhanjil, Surinder K. and Nicolaides, Andrew and Sharma, Aditya and Rathore, Vijay and Fatemi, Mostafa and Alizad, Azra and Krishnan, Pudukode R. and Yadav, Rajanikant R. and Nagy, Frence and Kincses, Zsigmond Tam\'{a}s and Ruzsa, Zoltan and Naidu, Subbaram and Vi\v{s}kovi\'{c}, Klaudija and Kalra, Manudeep K. and Suri, Jasjit S.}, year = {2022}, pages = {34}, DOI = {10.1016/j.compbiomed.2022.105571}, chapter = {34}, keywords = {AI, COVID-19, Deep learning, Glass ground opacities, Hounsfield units, Lung CT, Lung segmentation, Pruning}, journal = {Computers in Biology and Medicine}, doi = {10.1016/j.compbiomed.2022.105571}, volume = {146}, issn = {0010-4825}, title = {Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0}, keyword = {AI, COVID-19, Deep learning, Glass ground opacities, Hounsfield units, Lung CT, Lung segmentation, Pruning}, chapternumber = {34} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE

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