PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation.
Comput Math Methods Med
; 2021: 6633755, 2021.
Article
in English
| MEDLINE | ID: covidwho-1140372
ABSTRACT
AIM:
COVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment.METHODS:
In this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model.RESULTS:
The 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches.CONCLUSION:
This proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia
/
Tuberculosis, Pulmonary
/
Diagnosis, Computer-Assisted
/
Neural Networks, Computer
/
Community-Acquired Infections
/
Imaging, Three-Dimensional
/
COVID-19
Type of study:
Diagnostic study
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Topics:
Long Covid
Limits:
Humans
Language:
English
Journal:
Comput Math Methods Med
Journal subject:
Medical Informatics
Year:
2021
Document Type:
Article
Affiliation country:
2021
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