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Knee Osteoarthritis Dataset with Severity Grading

Dataset Information

This article introduces a dataset containing knee joint X-ray data used for knee joint detection and grading according to the Kellgren–Lawrence (KL) grading system. The dataset comprises 9,786 knee joint images categorized into five severity levels based on the KL system: 0 (healthy), 1 (doubtful), 2 (mild), 3 (moderate), and 4 (severe). All images have a resolution of 224 × 224 pixels. Approximately 40% of the dataset images belong to the healthy category, 18% are classified as doubtful, 26% as mild, 13% as moderate, and slightly over 3% as severe.

Knee Osteoarthritis (KOA) is one of the most common diseases among older adults, caused by the wearing down of the articular cartilage in knee joints. The accuracy of severity diagnosis significantly depends on the clinician's diligence and experience. The low reliability of clinicians' grading is attributed to the very subtle differences between X-ray images of adjacent grades. Detection and diagnosis of KOA is one of the fields where Deep Learning (DL) technology is applied. After training, data is fed into models that predict the severity of KOA based on the KL grading system. The high prevalence of KOA necessitates an accurate, reliable, and automated severity classification system, and deep learning offers one such solution.

Dataset Meta Information

Dimensions Modality Task Type Anatomical Structures Number of Categories Data Volume File Format
2D X-Ray Classification Knee 5 9786 PNG

Resolution Details

Dataset Statistics size
min (224, 224)
median (224, 224)
max (224, 224)

Label Information Statistics

Severity Grade Label Image Count
Normal 0 3857
Doubtful 1 1770
Mild 2 2578
Moderate 3 1286
Severe 4 295

Visualization

0-4 represents different levels from normal to severe.

File Structure

Dataset
|-- images
|   |-- train
|      |-- 0
|        |-- xxx.png
|        |-- ...
|        |-- xxx.png
|      |-- 1
|        |-- xxx.png
|        |-- ...
|        |-- xxx.png
|      |-- ...
|      |-- 5
|        |-- xxx.png
|        |-- ...
|        |-- xxx.png
|   |-- val
|      |-- 0
|        |-- xxx.png
|        |-- ...
|        |-- xxx.png
|      |-- 1
|        |-- xxx.png
|        |-- ...
|        |-- xxx.png
|      |-- ...
|      |-- 5
|        |-- xxx.png
|        |-- ...
|        |-- xxx.png
|   |-- test
|      |-- 0
|        |-- xxx.png
|        |-- ...
|        |-- xxx.png
|      |-- 1
|        |-- xxx.png
|        |-- ...
|        |-- xxx.png
|      |-- ...
|      |-- 5
|        |-- xxx.png
|        |-- ...
|        |-- xxx.png

Authors and Institutions

Abdul Sami Mohammed (Prince Mohammad Bin Fahd University)

Ahmed Abul Hasanaath (Prince Mohammad Bin Fahd University)

Source Information

Official Website: https://data.mendeley.com/datasets/56rmx5bjcr/1

Download Link: https://data.mendeley.com/datasets/56rmx5bjcr/1

Article Address: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137589/

Publication Date: 2020.6.23

Citation

Chen, Pingjun (2018), “Knee Osteoarthritis Severity Grading Dataset”, Mendeley Data, V1, doi: 10.17632/56rmx5bjcr.1

Original introduction article is here.