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Diabetic Retinopathy Debrecen Data Set Data Set
Download: Data Folder, Data Set Description
Abstract: This dataset contains features extracted from the Messidor image set to predict whether an image contains signs of diabetic retinopathy or not.
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Data Set Characteristics: |
Multivariate |
Number of Instances: |
1151 |
Area: |
Life |
Attribute Characteristics: |
Integer, Real |
Number of Attributes: |
20 |
Date Donated |
2014-11-03 |
Associated Tasks: |
Classification |
Missing Values? |
N/A |
Number of Web Hits: |
157837 |
Source:
1. Dr. Balint Antal, Department of Computer Graphics and Image Processing
Faculty of Informatics, University of Debrecen, 4010, Debrecen, POB 12, Hungary
antal.balint '@' inf.unideb.hu
2. Dr. Andras Hajdu, Department of Computer Graphics and Image Processing
Faculty of Informatics, University of Debrecen, 4010, Debrecen, POB 12, Hungary
hajdu.andras '@' inf.unideb.hu
Data Set Information:
This dataset contains features extracted from the Messidor image set to predict whether an image contains signs of diabetic retinopathy or not. All features represent either a detected lesion, a descriptive feature of a anatomical part or an image-level descriptor. The underlying method image analysis and feature extraction as well as our classification technique is described in Balint Antal, Andras Hajdu: An ensemble-based system for automatic screening of diabetic retinopathy, Knowledge-Based Systems 60 (April 2014), 20-27. The image set (Messidor) is available at [Web Link].
Attribute Information:
0) The binary result of quality assessment. 0 = bad quality 1 = sufficient quality.
1) The binary result of pre-screening, where 1 indicates severe retinal abnormality and 0 its lack.
2-7) The results of MA detection. Each feature value stand for the
number of MAs found at the confidence levels alpha = 0.5, . . . , 1, respectively.
8-15) contain the same information as 2-7) for exudates. However,
as exudates are represented by a set of points rather than the number of
pixels constructing the lesions, these features are normalized by dividing the
number of lesions with the diameter of the ROI to compensate different image
sizes.
16) The euclidean distance of the center of
the macula and the center of the optic disc to provide important information
regarding the patient’s condition. This feature
is also normalized with the diameter of the ROI.
17) The diameter of the optic disc.
18) The binary result of the AM/FM-based classification.
19) Class label. 1 = contains signs of DR (Accumulative label for the Messidor classes 1, 2, 3), 0 = no signs of DR.
Relevant Papers:
Provide references to papers that have cited this data set in the past (if any).
Citation Request:
Please cite the following paper: Balint Antal, Andras Hajdu: An ensemble-based system for automatic screening of diabetic retinopathy, Knowledge-Based Systems 60 (April 2014), 20-27.
The dataset is based on features extracted from the Messidor image dataset: [Web Link].
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