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Related Experiment Video

Updated: Dec 13, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Introducing the GEV Activation Function for Highly Unbalanced Data to Develop COVID-19 Diagnostic Models.

Joshua Bridge, Yanda Meng, Yitian Zhao

IEEE Journal of Biomedical and Health Informatics

|August 6, 2020

Summary

This summary is machine-generated.

A new generalized extreme value (GEV) activation function improves deep learning for COVID-19 diagnosis using chest X-rays. This method excels with unbalanced datasets, outperforming traditional sigmoid functions for accurate disease detection.

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Area of Science:

Medical Imaging
Artificial Intelligence
Machine Learning

Background:

Accurate COVID-19 diagnosis is critical for pandemic control.
Limited availability of diagnostic images hinders deep learning algorithm development.
Traditional deep learning struggles with imbalanced datasets.

Purpose of the Study:

To introduce a novel activation function for improved deep learning performance in medical image analysis.
To address the challenge of highly unbalanced datasets in disease diagnosis.
To enhance the accuracy of COVID-19 detection using chest X-rays.

Main Methods:

Developed a novel activation function based on the generalized extreme value (GEV) distribution.
Applied the GEV activation function to binary classification tasks with imbalanced medical imaging data.
Validated the GEV activation function against the traditional sigmoid activation function.

Main Results:

The GEV activation function demonstrated superior performance compared to the sigmoid function on imbalanced datasets.
Achieved improved area under the receiver operating characteristic curve (DeLong's p-value < 0.05) in COVID-19 chest X-ray classification.
Showcased enhanced sensitivity on datasets including pneumonia vs. COVID-19 X-rays and CT images.

Conclusions:

The proposed GEV activation function offers a significant improvement over sigmoid activation for binary classification, especially with limited training data.
This novel approach holds promise for advancing automated diagnosis of COVID-19 and other diseases.
The GEV activation function represents a new paradigm for handling imbalanced data in medical AI.