H3DNN: 3D Deep Learning Based Detection of COVID-19 Virus using Lungs Computed Tomography

Khan, Abdullah; Shafiq, Sidra; Kumar, Rajesh; Kumar, Jay; Haq, Amin Ul

doi:10.1109/iccwamtip51612.2020.9317357

Cited by 19 publications

(7 citation statements)

References 18 publications

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“…This is shown in the red color lesion as an overlay image with a grayscale CT image in the background. When it comes to COVID-19 lung lesion detection and quantification in CT, Suri et al [ 217 , 218 ] have demonstrated the usage of hybrid DL (HDL) models vs. solo DL (SDL) models, exhibiting its superiority for lung lesion segmentation in CT scans. One of the most important aspects of DL is the optimization of hyperparameters during training to obtain the best performance of the DL system.…”

Section: Deep Learning For Cvd/stroke Risk Assessment In Pd Patients ...mentioning

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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Section: Deep Learning For Cvd/stroke Risk Assessment In Pd Patients ...mentioning

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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“…Attributes of the Nodes/ p,q,∈ X (i) UX j * Attributes of Nodes * log(X (i) , X j ) (13) where X(i) and X(j) are the neighborhood hospitals located at i th and j th position which are differentiated by their unique ID.…”

Section: Blockchain-based Data Retrieval Processmentioning

confidence: 99%

“…The diagnosis of COVID-19 disorders relies heavily on clinical symptoms, epidemiological history, computed tomography (CT), and pathogenic testing. Most of the patients with COVID-19 show the same visual symptoms on CT scans as the other lung disorders (9)(10)(11)(12)(13) despite the use of a variety of radiological modalities. Symptoms of COVID-19 vary widely across individuals, and the disease is growing at an alarming rate (14).…”

Section: Introductionmentioning

confidence: 99%

FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction

Durga

Poovammal

2022

Front. Public Health

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With the SARS-CoV-2's exponential growth, intelligent and constructive practice is required to diagnose the COVID-19. The rapid spread of the virus and the shortage of reliable testing models are considered major issues in detecting COVID-19. This problem remains the peak burden for clinicians. With the advent of artificial intelligence (AI) in image processing, the burden of diagnosing the COVID-19 cases has been reduced to acceptable thresholds. But traditional AI techniques often require centralized data storage and training for the predictive model development which increases the computational complexity. The real-world challenge is to exchange data globally across hospitals while also taking into account of the organizations' privacy concerns. Collaborative model development and privacy protection are critical considerations while training a global deep learning model. To address these challenges, this paper proposes a novel framework based on blockchain and the federated learning model. The federated learning model takes care of reduced complexity, and blockchain helps in distributed data with privacy maintained. More precisely, the proposed federated learning ensembled deep five learning blockchain model (FLED-Block) framework collects the data from the different medical healthcare centers, develops the model with the hybrid capsule learning network, and performs the prediction accurately, while preserving the privacy and shares among authorized persons. Extensive experimentation has been carried out using the lung CT images and compared the performance of the proposed model with the existing VGG-16 and 19, Alexnets, Resnets-50 and 100, Inception V3, Densenets-121, 119, and 150, Mobilenets, SegCaps in terms of accuracy (98.2%), precision (97.3%), recall (96.5%), specificity (33.5%), and F1-score (97%) in predicting the COVID-19 with effectively preserving the privacy of the data among the heterogeneous users.

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“…The latest report of the World Health Organization (WHO) reveals that COVID-19 is an infectious disease that primarily affects the lungs such as SARS, giving them a honeycomb-like ap pearance [29] . Even after recovering from COVID-19, some patients have to live with permanent lung damage [30] , [31] . The first motivation of our work is to find small infected areas in the lungs due to COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging

et al. 2021

Self Cite

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With the increase of COVID-19 cases worldwide, an effective way is required to diagnose COVID-19 patients. The primary problem in diagnosing COVID-19 patients is the shortage and reliability of testing kits, due to the quick spread of the virus, medical practitioners are facing difficulty in identifying the positive cases. The second real-world problem is to share the data among the hospitals globally while keeping in view the privacy concerns of the organizations. Building a collaborative model and preserving privacy are the major concerns for training a global deep learning model. This paper proposes a framework that collects a small amount of data from different sources (various hospitals) and trains a global deep learning model using blockchain-based federated learning. Blockchain technology authenticates the data and federated learning trains the model globally while preserving the privacy of the organization. First, we propose a data normalization technique that deals with the heterogeneity of data as the data is gathered from different hospitals having different kinds of Computed Tomography (CT) scanners. Secondly, we use Capsule Network-based segmentation and classification to detect COVID-19 patients. Thirdly, we design a method that can collaboratively train a global model using blockchain technology with federated learning while preserving privacy. Additionally, we collected real-life COVID-19 patients' data open to the research community. The proposed framework can utilize up-to-date data which improves the recognition of CT images. Finally, we conducted comprehensive experiments to validate the proposed method. Our results demonstrate better performance for detecting COVID-19 patients.

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H3DNN: 3D Deep Learning Based Detection of COVID-19 Virus using Lungs Computed Tomography

Cited by 19 publications

References 18 publications

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

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

FLED-Block: Federated Learning Ensembled Deep Learning Blockchain Model for COVID-19 Prediction

Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging

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