作者
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 Mavrogeni, John 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
发表日期
2022/7/1
期刊
Computers in biology and medicine
卷号
146
页码范围
105571
出版商
Pergamon
简介
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 …
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M Agarwal, S Agarwal, L Saba, GL Chabert, S Gupta… - Computers in biology and medicine, 2022