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 Kumar; Carriero, Alessandro; Paschè, Alessio; Danna, Pietro; Mehmedović, Armin; Faa, Gavino; Shrivastava, Saurabh RamBihariLal; Jain, Kanishka; Jain, Harsh; Jujaray, Tanay; Singh, Inder; Turk, Monika; Chadha, Paramjit S.; Johri, Amer M.; Khanna, Narendra N.; Mavrogeni, Sophie; Laird, John R.; Sobel, David; 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; Nicolaides, Andrew; Sharma, Aditya; Rathore, V. S.; Fatemi, Mostafa; Alizad, Azra; Krishnan, Prasad; Yadav, Rajanikant R; Frence, Nagy; Kincses, Zsigmond Tamás; Ruzsa, Zoltán; Naidu, Subbaram; Višković, Klaudija; Kalra, Manudeep; Suri, Jasjit S.

doi:10.1016/j.compbiomed.2022.105571

Cited by 43 publications

(29 citation statements)

References 98 publications

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“…Grad-CAM builds the coarse localization map using the gradients of the target (COVID-19 in the Xception-based classification model), thereby showing the critical regions in the form of heatmap scans. It uses the final convolution layer to produce the heatmap [ 64 ].…”

Section: Resultsmentioning

confidence: 99%

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Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans

et al. 2022

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Background and Motivation: COVID-19 has resulted in a massive loss of life during the last two years. The current imaging-based diagnostic methods for COVID-19 detection in multiclass pneumonia-type chest X-rays are not so successful in clinical practice due to high error rates. Our hypothesis states that if we can have a segmentation-based classification error rate <5%, typically adopted for 510 (K) regulatory purposes, the diagnostic system can be adapted in clinical settings. Method: This study proposes 16 types of segmentation-based classification deep learning-based systems for automatic, rapid, and precise detection of COVID-19. The two deep learning-based segmentation networks, namely UNet and UNet+, along with eight classification models, namely VGG16, VGG19, Xception, InceptionV3, Densenet201, NASNetMobile, Resnet50, and MobileNet, were applied to select the best-suited combination of networks. Using the cross-entropy loss function, the system performance was evaluated by Dice, Jaccard, area-under-the-curve (AUC), and receiver operating characteristics (ROC) and validated using Grad-CAM in explainable AI framework. Results: The best performing segmentation model was UNet, which exhibited the accuracy, loss, Dice, Jaccard, and AUC of 96.35%, 0.15%, 94.88%, 90.38%, and 0.99 (p-value <0.0001), respectively. The best performing segmentation-based classification model was UNet+Xception, which exhibited the accuracy, precision, recall, F1-score, and AUC of 97.45%, 97.46%, 97.45%, 97.43%, and 0.998 (p-value <0.0001), respectively. Our system outperformed existing methods for segmentation-based classification models. The mean improvement of the UNet+Xception system over all the remaining studies was 8.27%. Conclusion: The segmentation-based classification is a viable option as the hypothesis (error rate <5%) holds true and is thus adaptable in clinical practice.

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Section: Resultsmentioning

confidence: 99%

“…The second reason was an easy interface between the segmentation and the classification pipeline. Our future objective is to move the desktop-based design to cloud-based framework and therefore these high performing classifiers could be the ideal choice for our cloud-based system design [ 64 , 65 ].…”

Section: Methodsmentioning

confidence: 99%

Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans

et al. 2022

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“…There have been two significant developments in the field of AI that cannot be ignored, namely, (a) pruning of AI (PAI) models and (b) explainable AI (XAI). The concept of pruning AI is motivated by the amount of storage needed during the training process of the AI models [ 149 ]. In contrast, the concept of explainable AI is inspired by the process of knowing how the AI black box performs [ 150 , 151 ].…”

Section: Critical Discussionmentioning

confidence: 99%

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

Jena

Saxena

Nayak

et al. 2022

Cancers

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Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of “radiomics and genomics” has been considered under the umbrella of “radiogenomics”. Furthermore, AI in a radiogenomics’ environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor’s characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.

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“…For CVD/stroke risk prediction in PD patients in the COVID-19 framework, the same set of AI benefits apply here as well. The deep learning automated model can be used for COVID-19 detection and quantification in CT scans [ 286 ]. This is fully automated and a superior time-saver, which translates into cost savings.…”

Section: Critical Discussionmentioning

confidence: 99%

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Suri¹,

Maindarkar

Sauseng

et al. 2022

Diagnostics

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Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19.Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

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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

Cited by 43 publications

References 98 publications

Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans

Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

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