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Pregled bibliografske jedinice broj: 1226993

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

Suri, Jasjit S.; Agarwal, Sushant; Chabert, Gian Luca; Carriero, Alessandro; Paschè, Alessio; Danna, Pietro S. C.; Saba, Luca; Mehmedović, Armin; Faa, Gavino; Singh, Inder M. et al.
COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans // Diagnostics, 12 (2022), 6; 40, 40 doi:10.3390/diagnostics12061482 (međunarodna recenzija, članak, znanstveni)

CROSBI ID: 1226993 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

Autori
Suri, Jasjit S. ; Agarwal, Sushant ; Chabert, Gian Luca ; Carriero, Alessandro ; Paschè, Alessio ; Danna, Pietro S. C. ; Saba, Luca ; Mehmedović, Armin ; Faa, Gavino ; Singh, Inder M. ; Turk, Monika ; Chadha, Paramjit S. ; Johri, Amer M. ; Khanna, Narendra N. ; Mavrogeni, Sophie ; Laird, John R. ; Pareek, Gyan ; Miner, Martin ; Sobel, David W. ; Balestrieri, Antonella ; Sfikakis, Petros P. ; Tsoulfas, George ; Protogerou, Athanasios D. ; 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. ; Nagy, Ferenc ; Ruzsa, Zoltan ; Fouda, Mostafa M. ; Naidu, Subbaram ; Višković, Klaudija ; Kalra, Mannudeep K.

Izvornik
Diagnostics (2075-4418) 12 (2022), 6; 40, 40

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

Ključne riječi
COVID-19 lesion ; lung CT ; Hounsfield units ; glass ground opacities ; hybrid deep learning ; explainable AI ; segmentation ; classification ; GRAD-CAM ; Grad-CAM++ ; Score-CAM ; FasterScore-CAM

Sažetak
Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the “COVLIAS 2.0-cXAI” system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet- UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient- weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.

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.mdpi.com

Citiraj ovu publikaciju:

Suri, Jasjit S.; Agarwal, Sushant; Chabert, Gian Luca; Carriero, Alessandro; Paschè, Alessio; Danna, Pietro S. C.; Saba, Luca; Mehmedović, Armin; Faa, Gavino; Singh, Inder M. et al.
COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans // Diagnostics, 12 (2022), 6; 40, 40 doi:10.3390/diagnostics12061482 (međunarodna recenzija, članak, znanstveni)
Suri, J., Agarwal, S., Chabert, G., Carriero, A., Paschè, A., Danna, P., Saba, L., Mehmedović, A., Faa, G. & Singh, I. (2022) COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans. Diagnostics, 12 (6), 40, 40 doi:10.3390/diagnostics12061482.
@article{article, author = {Suri, Jasjit S. and Agarwal, Sushant and Chabert, Gian Luca and Carriero, Alessandro and Pasch\`{e}, Alessio and Danna, Pietro S. C. and Saba, Luca and Mehmedovi\'{c}, Armin and Faa, Gavino 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 Pareek, Gyan and Miner, Martin and Sobel, David W. and Balestrieri, Antonella and Sfikakis, Petros P. and Tsoulfas, George and Protogerou, Athanasios D. 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 Nagy, Ferenc and Ruzsa, Zoltan and Fouda, Mostafa M. and Naidu, Subbaram and Vi\v{s}kovi\'{c}, Klaudija and Kalra, Mannudeep K.}, year = {2022}, pages = {40}, DOI = {10.3390/diagnostics12061482}, chapter = {40}, keywords = {COVID-19 lesion, lung CT, Hounsfield units, glass ground opacities, hybrid deep learning, explainable AI, segmentation, classification, GRAD-CAM, Grad-CAM++, Score-CAM, FasterScore-CAM}, journal = {Diagnostics}, doi = {10.3390/diagnostics12061482}, volume = {12}, number = {6}, issn = {2075-4418}, title = {COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans}, keyword = {COVID-19 lesion, lung CT, Hounsfield units, glass ground opacities, hybrid deep learning, explainable AI, segmentation, classification, GRAD-CAM, Grad-CAM++, Score-CAM, FasterScore-CAM}, chapternumber = {40} }
@article{article, author = {Suri, Jasjit S. and Agarwal, Sushant and Chabert, Gian Luca and Carriero, Alessandro and Pasch\`{e}, Alessio and Danna, Pietro S. C. and Saba, Luca and Mehmedovi\'{c}, Armin and Faa, Gavino 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 Pareek, Gyan and Miner, Martin and Sobel, David W. and Balestrieri, Antonella and Sfikakis, Petros P. and Tsoulfas, George and Protogerou, Athanasios D. 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 Nagy, Ferenc and Ruzsa, Zoltan and Fouda, Mostafa M. and Naidu, Subbaram and Vi\v{s}kovi\'{c}, Klaudija and Kalra, Mannudeep K.}, year = {2022}, pages = {40}, DOI = {10.3390/diagnostics12061482}, chapter = {40}, keywords = {COVID-19 lesion, lung CT, Hounsfield units, glass ground opacities, hybrid deep learning, explainable AI, segmentation, classification, GRAD-CAM, Grad-CAM++, Score-CAM, FasterScore-CAM}, journal = {Diagnostics}, doi = {10.3390/diagnostics12061482}, volume = {12}, number = {6}, issn = {2075-4418}, title = {COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans}, keyword = {COVID-19 lesion, lung CT, Hounsfield units, glass ground opacities, hybrid deep learning, explainable AI, segmentation, classification, GRAD-CAM, Grad-CAM++, Score-CAM, FasterScore-CAM}, chapternumber = {40} }

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

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