Dataset Information

PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis.

ABSTRACT: Objective: COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system. Methods: First, the n-conv module (nCM) is introduced. Then we built a 12-layer convolutional neural network (12l-CNN) as the backbone network. Afterwards, PatchShuffle was introduced to integrate with 12l-CNN as a regularization term of the loss function. Our model was named PSCNN. Moreover, multiple-way data augmentation and Grad-CAM are employed to avoid overfitting and locating lung lesions. Results: The mean and standard variation values of the seven measures of our model were 95.28 ± 1.03 (sensitivity), 95.78 ± 0.87 (specificity), 95.76 ± 0.86 (precision), 95.53 ± 0.83 (accuracy), 95.52 ± 0.83 (F1 score), 91.7 ± 1.65 (MCC), and 95.52 ± 0.83 (FMI). Conclusion: Our PSCNN is better than 10 state-of-the-art models. Further, we validate the optimal hyperparameters in our model and demonstrate the effectiveness of PatchShuffle.

SUBMITTER: Wang SH

PROVIDER: S-EPMC8585997 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis.

Wang Shui-Hua SH Zhu Ziquan Z Zhang Yu-Dong YD

Frontiers in public health 20211029

Objective: COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system. Methods: First, the n-conv module (nCM) is introduced. Then we built a 12-layer convolutional neural network (12l-CNN) as the backbone network. Afterwards, PatchShuffle was introduced to integrate with 12l-CNN as a regularization term of the loss function. Our model was named PSCNN. Moreover, multiple-way data augmen ...[more]

PMID: 34778194

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Project description:Background and objectiveSARS-CoV-2, a novel strain of coronavirus' also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a "pandemic". Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths. Undoubtedly the world was rocked by the COVID-19 pandemic, but researchers rose to all manner of challenges to tackle this pandemic by adopting the shreds of evidence of ML and AI in previous epidemics to develop novel models, methods, and strategies. We aim to provide a deeper insight into the convolutional neural network which is the most notable and extensively adopted technique on radiographic visual imagery to help expert medical practitioners and researchers to design and finetune their state-of-the-art models for their applicability in the arena of COVID-19.MethodIn this study, a deep convolutional neural network, its layers, activation and loss functions, regularization techniques, tools, methods, variants, and recent developments were explored to find its applications for COVID-19 prognosis. The pipeline of a general architecture for COVID-19 prognosis has also been proposed.ResultThis paper highlights recent studies of deep CNN and its applications for better prognosis, detection, classification, and screening of COVID-19 to help researchers and expert medical community in multiple directions. It also addresses a few challenges, limitations, and outlooks while using such methods for COVID-19 prognosis.ConclusionThe recent and ongoing developments in AI, MI, and deep learning (Deep CNN) has shown promising results and significantly improved performance metrics for screening, prediction, detection, classification, forecasting, medication, treatment, contact tracing, etc. to curtail the manual intervention in medical practice. However, the research community of medical experts is yet to recognize and label the benchmark of the deep learning framework for effective detection of COVID-19 positive cases from radiology imagery.

Dataset Information

PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis.

Publications

PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis.

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