In this project, I developed a deep learning model to for binary classification of chest X-rays (pneumonia or normal), using the widely-used Chest X-ray dataset from Kaggle. The model is built on MobileNet, a lightweight and efficient convolutional neural network architecture designed for mobile and embedded vision apps. I obtained 93% accuracy.
The data can be found here: https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
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.
Result, Confusion Matrix
Data: https://data.mendeley.com/datasets/rscbjbr9sj/2
License: CC BY 4.0
Citation: http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5