Progressive and Combined Deep Transfer Learning for pneumonia diagnosis in chest X-ray images

Pneumonia is a life-threatening disease that occurs in the lungs and is caused by a bacterial or viral infection. However, it is very difficult to diagnose it by simply looking at chest x-rays, because it is necessary to improve diagnostic accuracy. This study aims to simplify the process of detecting and classifying pneumonia for both experts and patients, using a dataset containing 5247 CXR images. Five different pretrained CNNs: AlexNet, VGG-16, ResNet50, DenseNet-121 and InceptionV3 were used separately or together for transfer learning in a progressive way. Firstly, they are pretrained on ImageNet dataset, and secondly, on a radiographic images which concerns another disease (available in medium size with a nature close to our base). These models are refined according to different fine-tuning levels and strategies. A weighted classifier-based approach is introduced to combine their weighted prediction. The results obtained show the possibility of moving easily from the classification of a disease to another using a progressive transfer learning, which has a limited number of images by taking advantage of the knowledge already acquired on another very large base.