Multi-Class Knee Osteoporosis X-ray Dataset

This dataset contains knee radiographic (X-ray) images collected and reorganized for osteoporosis classification research using deep learning techniques. The primary dataset was obtained from Kaggle (https://www.kaggle.com/datasets/866059b7930a5c49cd77d94c1761840a19d88074cad74e8f0e0cfa2b236a6904) and consists of three classes representing bone density conditions: Normal, Osteopenia, and Osteoporosis. The dataset contains a total of 1,947 knee X-ray images, including 780 Normal images, 793 Osteoporosis images, and 374 Osteopenia images, making it suitable for osteoporosis classification and medical image analysis tasks.In addition, the Knee X-ray Osteoporosis Database dataset was also used and this dataset was collected by Insha Majeed Wani and Sakshi Arora from Shri Mata Vaishno Devi University, this dataset was also published on Mendeley Data https://data.mendeley.com/datasets/fxjm8fb6mw/2. The Knee X-ray Osteoporosis Database contains 350 knee X-ray images divided into three classes: Normal, Osteoporosis, and Osteopenia. This dataset is imbalanced in class distribution with only 36 normal images, 154 osteopeni,a and 49 osteoporosis.

Since both datasets exhibited class imbalance, several data augmentation techniques were applied to improve class distribution and increase data diversity. These augmentation methods included random horizontal and vertical flipping and random image rotations at different angles. Augmentation was performed to generate balanced image samples across all classes and improve the robustness of the deep learning model. After preprocessing and augmentation, each class was balanced to contain an equal number of images.

The final combined dataset was divided into training, validation, and testing subsets using a ratio of 70%, 20%, and 10%, respectively. The prepared dataset was then used for osteoporosis classification using deep learning techniques on knee radiographic images.