Hybridation of ResNet 50 and a customized CNN using transfert learning for pneumonia detection in CXR images

In this paper, to categorize and detect Pneumonia desease from a collection of chest X-ray picture samples, we propose a deep learning technique based on object detection, convolutional neural networks, and transfer learning. The proposed model is a hybridation of to models the pre-trained model (ResNet -50) and our designed architecture. The Guangzhou Women and Children's Medical Center in Guangzhou, China provided the chest X-ray dataset used in this study. There are 5,000 samples in the data set, with 1,583 healthy samples and 4,273 pneumonia samples). Preprocessing techniques such as Bilateral Equalization was also used to improve accuracy. Due to the imbalance of the data set, we adopted some training techniques to improve the learning process of the samples. This network achieved over 99% accuracy due to the proposed architecture that is based on a combination of two models. The pre-trained ResNet 50 as feature extractor and our designed convolutional neural network (CNN).