Identification of pneumonia on chest x-ray images through machine learning

TL;DR

This study developed a machine learning-based software using transfer learning to accurately identify pneumonia in chest X-ray images, achieving high sensitivity and specificity, aiding early diagnosis and treatment.

Contribution

The paper presents a novel machine learning model with transfer learning for pneumonia detection in chest X-rays, demonstrating high accuracy on a pediatric dataset.

Findings

Achieved 98% sensitivity in pneumonia detection.

Achieved 97.3% specificity in pneumonia detection.

Proved feasibility of automated pneumonia diagnosis from X-ray images.

Abstract

Pneumonia is the leading infectious cause of infant death in the world. When identified early, it is possible to alter the prognosis of the patient, one could use imaging exams to help in the diagnostic confirmation. Performing and interpreting the exams as soon as possible is vital for a good treatment, with the most common exam for this pathology being chest X-ray. The objective of this study was to develop a software that identify the presence or absence of pneumonia in chest radiographs. The software was developed as a computational model based on machine learning using transfer learning technique. For the training process, images were collected from a database available online with children's chest X-rays images taken at a hospital in China. After training, the model was then exposed to new images, achieving relevant results on identifying such pathology, reaching 98% sensitivity and 97.3% specificity for the sample used for testing. It can be concluded that it is possible to develop a software that identifies pneumonia in chest X-ray images.