COVID-19 Lung Detection using Keras Deep Learning Models

A novel coronavirus, known as COVID-19, is a recently discovered virus that primarily affects the upper respiratory tract and lungs. With the global pandemic reaching alarming proportions, the number of cases and fatalities continues to rise. In the medical field, chest X-ray images have emerged as a valuable tool for monitoring various lung disorders, including COVID-19. This research focuses on leveraging deep learning techniques, specifically convolutional neural networks (CNN) implemented with Keras, to accurately identify COVID-19 and normal chest X-ray images. The study involves feature extraction, fine-tuning of pretrained CNN models (VGG19, ResNet50V2, Xception, MobileNetV2, and DenseNet121), and training of a customized CNN model. The aim is to evaluate and compare the performance of these models in detecting COVID-19 lung abnormalities. The dataset consists of 455 images, including 221 COVID-19 chest X-ray images and 234 normal chest X-ray images. Accuracy testing is used to assess the classification performance of the research. The experimental results demonstrate that deep learning models have the capability to effectively detect COVID-19 from chest X-ray images. These proposed models can serve as a foundation for future work, such as predicting accuracy and advancing diagnostic capabilities.