Research on CNN-based Models Optimized by Genetic Algorithm and Application in the Diagnosis of Pneumonia and COVID-19
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Abstract
In this research, an optimized deep learning method was proposed to explore the possibility and practicality of neural network applications in medical imaging. The method was used to achieve the goal of judging common pneumonia and even COVID-19 more effectively. Where, the genetic algorithm was taken advantage to optimize the Dropout module, which is essential in neural networks so as to improve the performance of typical neural network models. The experiment results demonstrate that the proposed method shows excellent performance and strong practicability in judging pneumonia, and the application of advanced artificial intelligence technology in the field of medical imaging has broad prospects.
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SciScore for 10.1101/2020.04.21.20072637: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources In the field of medical imaging, the research group of Wuhan University used ResNet to study the classification of human protein atlas images (Chang, 2019). ResNetsuggested: (RESNET, RRID:SCR_002121)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial …
SciScore for 10.1101/2020.04.21.20072637: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources In the field of medical imaging, the research group of Wuhan University used ResNet to study the classification of human protein atlas images (Chang, 2019). ResNetsuggested: (RESNET, RRID:SCR_002121)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.04.21.20072637: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources the paper COVID-CT-Dataset: A CT Scan Dataset about COVID-19, the researchers extracted CT images from 760 papers which from MedRxiv and BioRxiv related to COVID19. BioRxivsuggested: (bioRxiv, SCR_003933)In the field of medical imaging , the research group of Wuhan University used ResNet to study the classification of human protein atlas images ( Chang , 2019) . ResNetsuggested: (RESNET, SCR_002121)Some scholars from UCSD submitted a paper in Arxiv on 2020.03.30, … SciScore for 10.1101/2020.04.21.20072637: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources the paper COVID-CT-Dataset: A CT Scan Dataset about COVID-19, the researchers extracted CT images from 760 papers which from MedRxiv and BioRxiv related to COVID19. BioRxivsuggested: (bioRxiv, SCR_003933)In the field of medical imaging , the research group of Wuhan University used ResNet to study the classification of human protein atlas images ( Chang , 2019) . ResNetsuggested: (RESNET, SCR_002121)Some scholars from UCSD submitted a paper in Arxiv on 2020.03.30, which not only disclosed the dataset they collected, but also used a pre-trained DenseNet to train a convolutional neural network model and achieve an F1 of 0.85. Arxivsuggested: (arXiv, SCR_006500)Results from OddPub: We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).
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