Prediction of Lung Infections with Deep Learning Techniques: A Systematic Review

This research study systematically examines the application of deep learning techniques in predicting lung infections including cancer, pneumonia, tuberculosis, Covid 19 and so on. The analysis includes a range of deep learning models, including convolutional neural networks (CNNs) and various architectures like ResNet, VGG, AlexNet, and others. The review assesses the efficacy of these techniques in forecasting lung infections on their computational complexity and accuracy. This study also diversified by utilizing both chest CT images and X-ray images on the predictions. The insights derived from this systematic review contribute to a better understanding of the potential and limitations of employing deep learning in the early detection of lung infections.