Pneumonia Classification and Localization in Enhanced X-ray Images with Transfer Learning and CNN Architectures

Pneumonia is a pulmonary inflammation that can be caused by a variety of germs: bacteria, viruses, or fungi. Worldwide, pneumonia is a common disease responsible for an estimated $15 \%$ of deaths in kids under 5 years old. In general, the risk of pulmonary infection increases steadily after the age of 40 and is particularly high among individuals over 65. Despite the progress made in treating pulmonary infections, pneumonia remains a major cause of hospitalization and requires particular attention concerning efficient diagnosis and treatment. This study is built on a dataset made up of 5863 JPEG X-ray chest images. Through different convolutional neural network architectures based on transfer learning associated with data augmentation and various image enhancements such as Noise 2 void denoising and histogram equalization. We have implemented several models for pneumonia detection and localization; most of these networks are extremely promising in terms of performance, and most of the time they accurately exceed $98 \%$ in all metrics, which can simplify and speed up medical diagnosis, reduce pressure on hospitals, and save a lot of lives.