Related Experiment Video
Updated: Oct 22, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Deep Fractional Max Pooling Neural Network for COVID-19 Recognition
Shui-Hua Wang1, Suresh Chandra Satapathy2, Donovan Anderson1
1School of Mathematics and Actuarial Science, University of Leicester, Leicester, United Kingdom.
Insights
A new deep fractional max pooling neural network (DFMPNN) model offers efficient COVID-19 diagnosis. This AI approach achieves a 95.88% micro-averaged F1 score, outperforming existing methods.
Area of Science:
- Artificial Intelligence
- Medical Diagnostics
- Deep Learning
Background:
- Coronavirus disease 2019 (COVID-19) presents a significant global health challenge.
- Accurate and efficient diagnostic tools are crucial for disease management and control.
Purpose of the Study:
- To introduce a novel deep fractional max pooling neural network (DFMPNN) for enhanced COVID-19 diagnosis.
- To evaluate the performance of DFMPNN against state-of-the-art models.
Main Methods:
- A 12-layer DFMPNN model utilizing fractional max-pooling (FMP) instead of traditional max pooling (MP) and average pooling (AP).
- Implementation of multiple-way data augmentation (DA) to mitigate overfitting.
- Application of model averaging (MA) to reduce predictive randomness.
Main Results:
- The DFMPNN model was evaluated on a dataset comprising COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis (SPT), and healthy controls (HC).
- Achieved a micro-averaged F1 (MAF) score of 95.88% across 10 test runs.
- Fractional max-pooling (FMP) demonstrated superior performance compared to MP, AP, and L2-norm pooling (L2P).
Conclusions:
- The proposed DFMPNN model exhibits superior diagnostic performance for COVID-19 compared to 10 existing state-of-the-art methods.
- Fractional max-pooling is an effective pooling strategy for improving deep learning model accuracy in medical image analysis.
Abstract:
Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed "deep fractional max pooling neural network (DFMPNN)" to diagnose COVID-19 more efficiently. Methods: This 12-layer DFMPNN replaces max pooling (MP) and average pooling (AP) in ordinary neural networks with the help of a novel pooling method called "fractional max-pooling" (FMP). In addition, multiple-way data augmentation (DA) is employed to reduce overfitting. Model averaging (MA) is used to reduce randomness. Results: We ran our algorithm on a four-category dataset that contained COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis (SPT), and healthy control (HC). The 10 runs on the test set show that the micro-averaged F1 (MAF) score of our DFMPNN is 95.88%. Discussions: This proposed DFMPNN is superior to 10 state-of-the-art models. Besides, FMP outperforms traditional MP, AP, and L2-norm pooling (L2P).
