COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Deep Learning

Background:

• COVID-19 vaccines reduce severity but do not prevent infection, necessitating adaptive management strategies.
• Manual diagnosis of COVID-19 pneumonia from X-rays is labor-intensive, costly, and time-consuming.
• Automated diagnostic systems offer real-time, cost-effective solutions for COVID-19 detection.

Purpose of the Study:

• To propose a novel, optimized Deep Learning (DL) approach for automatic COVID-19 pneumonia classification and diagnosis using X-ray images.
• To enhance the accuracy and efficiency of COVID-19 diagnosis through advanced image processing and machine learning techniques.

Main Methods:

• Utilized a publicly available Kaggle dataset of 21,165 chest X-rays (Normal, COVID-19, Lung Opacity, Viral Pneumonia).
• Implemented a three-stage DL process: Image Enhancement, Data Augmentation, and Transfer Learning (AlexNet, GoogleNet, VGG16, VGG19, DenseNet).
• Employed VGG16 with frozen weights on an augmented, enhanced dataset for classification.

Main Results:

• Achieved a highest classification accuracy of 95.63% using the VGG16 transfer learning algorithm.
• The proposed DL approach demonstrated superior performance compared to existing methods in recent literature.
• Results indicate significant potential for automated, accurate COVID-19 pneumonia detection.

Conclusions:

• The optimized Deep Learning approach offers a highly accurate and efficient method for diagnosing COVID-19 pneumonia from X-ray images.
• This research contributes valuable insights into leveraging AI for improved public health diagnostics.
• Future work will focus on correlating DL model results with clinical observations to further refine diagnostic accuracy.