Deep Learning Approach for Analyzing the COVID-19 Chest X-Rays.

Area of Science:

• Medical Imaging Analysis
• Artificial Intelligence in Healthcare
• Radiology

Background:

• Convolutional Neural Networks (CNNs) and deep learning (DL) are increasingly utilized in medical image analysis.
• Accurate classification of X-ray images is crucial for diagnosing respiratory diseases like COVID-19 and viral pneumonia.

Purpose of the Study:

• To evaluate the effectiveness of a custom 9-layer CNN (9 LC) and two transfer learning models (VGG16, VGG19) for classifying X-ray images.
• To compare the diagnostic utility of these DL models in distinguishing between COVID-19, viral pneumonia, and normal X-ray findings.

Main Methods:

• Development and comparison of a 9-layer CNN model.
• Utilized two established transfer learning models: VGG16 and VGG19.
• Trained and validated models on two distinct datasets: Kaggle and institutional Radiodiagnosis department data.

Main Results:

• VGG16 achieved the highest accuracy across both datasets (94.96% on Kaggle, 85.71% on institutional data).
• The 9 LC and VGG19 models demonstrated superior precision for classifying X-ray images.
• All evaluated DL models showed good performance in differentiating disease categories.

Conclusions:

• Deep learning models, including CNNs and transfer learning, offer significant potential to assist radiologists in rapid disease prediction.
• These models can identify subtle pathological features in X-ray images that might be overlooked by human interpretation.
• DL is poised to play a pivotal role in the analysis of medical imaging datasets, enhancing diagnostic accuracy and efficiency.