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A computerized doughty predictor framework for corona virus disease: Combined deep learning based approach

IET Image Processing

|January 31, 2023

Summary

A deep learning framework was developed for predicting coronavirus disease. This approach combined multiple deep learning techniques for enhanced accuracy in disease prediction.

Area of Science:

Computer Science
Medical Imaging
Artificial Intelligence

Background:

Coronavirus disease (COVID-19) poses a significant global health challenge.
Accurate and timely prediction of infectious diseases is crucial for public health management.
Advancements in artificial intelligence offer potential for developing sophisticated diagnostic tools.

Purpose of the Study:

To develop a computerized framework for predicting coronavirus disease.
To leverage a combined deep learning approach for enhanced prediction accuracy.
To explore the efficacy of AI in disease prediction using medical imaging data.

Main Methods:

A novel framework integrating multiple deep learning models was designed.
The approach utilized medical imaging data for disease prediction.

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Specific deep learning architectures were combined to create a synergistic effect.

Main Results:

The developed framework demonstrated a high degree of accuracy in predicting coronavirus disease.
The combined deep learning approach outperformed individual models.
The system showed potential for early detection and management of the disease.

Conclusions:

A combined deep learning framework presents a promising tool for coronavirus disease prediction.
This AI-driven approach can aid in early diagnosis and public health response.
Further research can explore the integration of this framework into clinical settings.