Overview This project focuses on the detection of pneumonia from chest X-ray images using deep learning techniques. Pneumonia is a severe lung infection and early detection is crucial for effective treatment. Leveraging convolutional neural networks (CNNs), this project aims to build a model that can accurately classify X-ray images into categories: pneumonia or normal.
Features Preprocessing of X-ray images. Implementation of CNN-based deep learning model. Evaluation metrics for model performance. Visualization of training and validation processes. User-friendly interface for inference on new X-ray images.
Dataset The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal). Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care. For the analysis of chest x-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert.