Computer-Assisted Classification of Pneumonic Patients with Chest X-ray Using Deep Networks

The project aims to develop an accurate and efficient pneumonia detection system utilizing chest X-ray images, addressing the critical need for early diagnosis and treatment of pneumonia, particularly in resource-constrained healthcare settings, to improve patient outcomes and reduce healthcare costs. The approach used to identify pneumonia using chest X-rays has several stages. A large dataset of chest x-ray images from Kaggle is used to train and test the model. The images are preprocessed to assure quality and consistency. The model is trained on Convolutional Neural Networks (CNNs) and ResNet50 via transfer learning approaches. To improve model performance, data augmentation and fine-tuning will be used. For the CNN model, the accuracy, precision and F1-score are 95.13%, 96.57% and 95.38% respectively on the other hand for the ResNet50 model, the accuracy, precision and F1-score are 91.38%, 91.56% and 88.96% respectively. The proposed model can be used in computer-aided faster detection of pneumonia.