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Early Diagnosis of COVID-19 Images Using Optimal CNN Hyperparameters
Mohamed H Saad1, Sherief Hashima2, Wessam Sayed1
1Radiation Engineering Department, National Center for Radiation Research and Technology (NCRRT), Egyptian Atomic Energy Authority, Cairo 11787, Egypt.
Diagnostics (Basel, Switzerland)
|January 8, 2023
Summary
Optimizing convolutional neural network hyperparameters improves COVID-19 detection accuracy. This study enhanced diagnostic performance using grid search for learning rate and momentum in CNN models like ResNet, achieving over 98% accuracy.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Science
Background:
- Coronavirus disease (COVID-19) presents significant global health challenges.
- Current COVID-19 diagnostic test sensitivity is limited by specimen processing issues.
- Optimizing convolutional neural network (CNN) hyperparameters is crucial for enhancing diagnostic performance.
Purpose of the Study:
- To propose an optimization strategy for CNN hyperparameters, specifically learning rate and momentum, using grid search.
- To improve the performance and accuracy of COVID-19 detection through hyperparameter optimization.
- To evaluate the effectiveness of optimized CNN architectures on diverse COVID-19 radiography datasets.
Main Methods:
- Implemented grid search to optimize learning rate and momentum for CNN hyperparameters.
- Utilized three CNN architectures: GoogleNet, VGG16, and ResNet.
- Tested models on two COVID-19 radiography datasets: Kaggle (X-ray) and China national center for bio-information (CT).
Main Results:
- Optimized CNN hyperparameters significantly improved disease classification accuracy.
- The proposed optimization technique outperformed previous methods across various metrics including accuracy, sensitivity, and specificity.
- Optimized ResNet achieved high classification accuracy: 98.98% for X-ray and 98.78% for CT images at epoch 25.
Conclusions:
- Hyperparameter optimization using grid search is an effective strategy for enhancing CNN performance in COVID-19 detection.
- Optimized CNN models demonstrate superior diagnostic capabilities compared to non-optimized models.
- The study highlights the potential of AI-driven approaches for accurate and sensitive COVID-19 diagnosis from radiographic images.
