End-to-end deep learning project using TenserFlow
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.
Download Link: https://kaggle.com/paultimothymooney/chest-xray-pneumonia
I've developed a Convolutional Neural Network to classify the X-Ray images into Normal and Pneumonial. Our model was around 92.47% accurate with the test images. More accuracy may have been acheived but due to lmited resources the model was trained only for 20 epochs Deep Learning will bring a great revolution in the field of healthcare in the coming years.