A Comparative Analysis of Deep Convolution Layered Machine Learning approaches for Detection of Pneumonia from Chest Radiographs

Pneumonia is a serious illness caused by bacteria, fungi, or viruses in the alveoli of the lungs. It starts with minor irregularities and progresses to the point when one or both lungs are afflicted. In many circumstances, failure to detect it in a timely manner might result in mortality. This illness is most frequent in young children between the age group of one and five years. For medical image diagnosis, radiographs (chest x-ray images) are the most basic examination tools. In the current work, the chest radio scans were collected and fed to the deep learning models for the feature extraction. Following feature extraction, the scans were sent into a machine learning classifiers, which classified the radiographs as normal or pneumonia-infected. The obtained results verify that adoption of CNN models helps in early diagnosis of pneumonia that results in securing the life of the patient.