Automated Diagnosis of Pneumonia using CNN and Transfer Learning Approaches

Abstract

Pneumonia is one of the most deadly diseases, especially for children below 5 years of age. To detect pneumonia radiologists, have to observe the chest x-ray and he/she has to update the doctor correctly which sometimes may not be accurate due to human error. The main objective of this paper is to identify if the person has Pneumonia or not with high accuracy. Automated diagnosis of pneumonia can be done with the help of CNN and Transfer Learning Approaches so that the person can get treatment as early as possible. The dataset used here is the chest X-ray (CXR) dataset based on a chest X-Ray scan database from paediatric patients from one to five years of age at the Guangzhou Women and Children’s Medical Centre. Deep Learning (CNN) and Transfer Learning Techniques along with Ensemble Learning have been implemented concluded that CNN achieved an accuracy of 89%, the Transfer Learning model achieved an accuracy of 93% and the ensemble model got an accuracy of 92%. Even though the highest accuracy is for the Transfer Learning model, considering all the other metrics like Recall, Support, and score, Ensemble has exhibited the best results.