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.; 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; Viskovic, Klaudija; Kalra, Mannudeep K.

doi:10.3390/diagnostics12061482

Submit to this Journal Review for this Journal Edit a Special Issue

Article Menu

...

Open AccessArticle

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

^1,2,*,

^2,3

⁴

⁵,

⁴,

⁴

⁴,

⁶,

⁷

¹,

⁸,

¹,

⁹,

¹⁰,

¹¹,

¹²,

¹³,

¹⁴,

¹³,

Antonella Balestrieri

⁴,

Petros P. Sfikakis

¹⁵,

George Tsoulfas

¹⁶

Athanasios D. Protogerou

¹⁷

¹⁸,

¹⁸,

^19,20,

²¹,

²²,

²³,

²⁴,

²⁵,

²³,

²⁶

²⁷

²⁸,

²⁹

³⁰,

³¹

³²

⁶ and

³³ Show full author list Hide full author list

Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA

Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA

Department of Computer Science Engineering, PSIT, Kanpur 209305, India

⁴

Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09123 Cagliari, Italy

⁵

Department of Radiology, “Maggiore della Carità” Hospital, University of Piemonte Orientale (UPO), Via Solaroli 17, 28100 Novara, Italy

⁶

Department of Radiology, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia

⁷

Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 09124 Cagliari, Italy

⁸

The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany

⁹

Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada

¹⁰

Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India

¹¹

Cardiology Clinic, Onassis Cardiac Surgery Center, 17674 Athens, Greece

¹²

Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA

¹³

Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA

¹⁴

Men’s Health Center, Miriam Hospital, Providence, RI 02912, USA

¹⁵

Rheumatology Unit, National Kapodistrian University of Athens, 17674 Athens, Greece

¹⁶

Department of Surgery, Aristoteleion University of Thessaloniki, 54124 Thessaloniki, Greece

¹⁷

Cardiovascular Prevention and Research Unit, Department of Pathophysiology, National & Kapodistrian University of Athens, 15772 Athens, Greece

¹⁸

Department of Immunology, SGPIMS, Lucknow 226014, India

¹⁹

Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK

²⁰

Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, UK

²¹

Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA

²²

Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON M5G 1N8, Canada

²³

AtheroPoint LLC., Roseville, CA 95661, USA

²⁴

Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Engomi 2408, Cyprus

²⁵

Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22902, USA

²⁶

Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA

²⁷

Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA

²⁸

Neurology Department, Fortis Hospital, Bengaluru 560076, India

²⁹

Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary

³⁰

Invasive Cardiology Division, University of Szeged, 1122 Budapest, Hungary

³¹

Department of ECE, Idaho State University, Pocatello, ID 83209, USA

³²

Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA

³³

Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA

Show full affiliation list

Hide full affiliation list

Author to whom correspondence should be addressed.

Academic Editor: Andor W.J.M. Glaudemans

Diagnostics 2022, 12(6), 1482; https://doi.org/10.3390/diagnostics12061482

Received: 24 May 2022 / Revised: 7 June 2022 / Accepted: 13 June 2022 / Published: 16 June 2022

(This article belongs to the Special Issue Lesion Detection and Analysis Using Artificial Intelligence)

View Full-Text Download PDF

Browse Figures

Citation Export

Abstract

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. View Full-Text

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

▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share and Cite

MDPI and ACS Style

Suri, J.S.; Agarwal, S.; Chabert, G.L.; Carriero, A.; Paschè, A.; Danna, P.S.C.; Saba, L.; Mehmedović, A.; Faa, G.; Singh, I.M.; Turk, M.; Chadha, P.S.; Johri, A.M.; Khanna, N.N.; Mavrogeni, S.; Laird, J.R.; Pareek, G.; Miner, M.; Sobel, D.W.; Balestrieri, A.; Sfikakis, P.P.; Tsoulfas, G.; Protogerou, A.D.; Misra, D.P.; Agarwal, V.; Kitas, G.D.; Teji, J.S.; Al-Maini, M.; Dhanjil, S.K.; Nicolaides, A.; Sharma, A.; Rathore, V.; Fatemi, M.; Alizad, A.; Krishnan, P.R.; Nagy, F.; Ruzsa, Z.; Fouda, M.M.; Naidu, S.; Viskovic, K.; Kalra, M.K. COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans. Diagnostics 2022, 12, 1482. https://doi.org/10.3390/diagnostics12061482

AMA Style

Suri JS, Agarwal S, Chabert GL, Carriero A, Paschè A, Danna PSC, Saba L, Mehmedović A, Faa G, Singh IM, Turk M, Chadha PS, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sobel DW, Balestrieri A, Sfikakis PP, Tsoulfas G, Protogerou AD, Misra DP, Agarwal V, Kitas GD, Teji JS, Al-Maini M, Dhanjil SK, Nicolaides A, Sharma A, Rathore V, Fatemi M, Alizad A, Krishnan PR, Nagy F, Ruzsa Z, Fouda MM, Naidu S, Viskovic K, Kalra MK. COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans. Diagnostics. 2022; 12(6):1482. https://doi.org/10.3390/diagnostics12061482

Chicago/Turabian Style

Suri, Jasjit S., Sushant Agarwal, Gian Luca Chabert, Alessandro Carriero, Alessio Paschè, Pietro S. C. Danna, Luca Saba, Armin Mehmedović, Gavino Faa, Inder M. Singh, Monika Turk, Paramjit S. Chadha, Amer M. Johri, Narendra N. Khanna, Sophie Mavrogeni, John R. Laird, Gyan Pareek, Martin Miner, David W. Sobel, Antonella Balestrieri, Petros P. Sfikakis, George Tsoulfas, Athanasios D. Protogerou, Durga Prasanna Misra, Vikas Agarwal, George D. Kitas, Jagjit S. Teji, Mustafa Al-Maini, Surinder K. Dhanjil, Andrew Nicolaides, Aditya Sharma, Vijay Rathore, Mostafa Fatemi, Azra Alizad, Pudukode R. Krishnan, Ferenc Nagy, Zoltan Ruzsa, Mostafa M. Fouda, Subbaram Naidu, Klaudija Viskovic, and Mannudeep K. Kalra. 2022. "COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans" Diagnostics 12, no. 6: 1482. https://doi.org/10.3390/diagnostics12061482

Find Other Styles

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Abstract views Pdf views Html views

Article Menu

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

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Article Access Map by Country/Region

Further Information

Guidelines

MDPI Initiatives

Follow MDPI