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

Mohit Agarwal, Sushant Agarwal, Luca Saba, Gian Luca Chabert, Suneet Gupta, Alessandro Carriero, Alessio Pasche, Pietro Danna, Armin Mehmedovic, Gavino Faa, Saurabh Shrivastava, Kanishka Jain, Harsh Jain, Tanay Jujaray, Inder M. Singh, Monika Turk, Paramjit S. Chadha, Amer M. Johri, Narendra N. Khanna, Sophie MavrogeniJohn R. Laird, David W. Sobel, Martin Miner, Antonella Balestrieri, Petros P. Sfikakis, George Tsoulfas, 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, Rajanikant R. Yadav, Frence Nagy, Zsigmond Tamás Kincses, Zoltan Ruzsa, Subbaram Naidu, Klaudija Viskovic, Manudeep K. Kalra, Jasjit S. Suri

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number105571
JournalComputers in Biology and Medicine
Volume146
DOIs
StatePublished - Jul 2022

Bibliographical note

Publisher Copyright:
© 2022

Keywords

  • AI
  • COVID-19
  • Deep learning
  • Glass ground opacities
  • Hounsfield units
  • Lung CT
  • Lung segmentation
  • Pruning
  • Neural Networks, Computer
  • Reproducibility of Results
  • Lung/diagnostic imaging
  • Tomography, X-Ray Computed/methods
  • COVID-19 Testing
  • Humans
  • Deep Learning
  • COVID-19/diagnostic imaging
  • Image Processing, Computer-Assisted/methods

PubMed: MeSH publication types

  • Multicenter Study
  • Journal Article

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