Cloud-Based Deep Learning for Pneumonia Detection: A Comprehensive Overview

At present, pneumonia is said to be one of the greatest health challenges across the world because it accounts for the deaths of millions every year, especially little children and aged people. The disease must be treated immediately, and although pneumonia is not difficult to cure, it is not properly diagnosed until its late stages. In addition, the absence of adequate diagnostic resources in developing countries and the variability in radiologist interpretations of diagnostic reports causes issues. A subfield of AI, deep learning, is very encouraging for the future of automated disease classification in medical images, most notably in chest X-rays (CXR). Such approaches, in combination with the benefits of cloud computing, may greatly enhance healthcare delivery systems by adding near-satellite coverage and reliable real-time data inputs. The content of this article is precisely targeted deep learning methodologies for the diagnosing of pneumonia on medical chest X-rays. Other effective image analysis algorithms that could assist in diagnosis are Convolutional Neural Networks, ResNet, DenseNet, and VGG Net. Due to their ability to extract complex features and provide accurate diagnosis deep learning algorithms have been helpful in analyzing medical images. Furthermore, services such as AWS, GCP, and Microsoft Azure are helpful in integrating deep learning algorithms by providing cloud computing resources like GPUs and TPUs.