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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
Published on: July 5, 2024
PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way
Shui-Hua Wang1,2, Yin Zhang3, Xiaochun Cheng4
1School of Computer Science, Henan Polytechnic University, China, Henan 454001, China.
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.
