Early Detection of Diabetic Retinopathy by Using Deep Learning Neural Network

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    This project presents a method to detect diabetic retinopathy on the fundus images by using deep learning neural network. Alexnet Convolution Neural Network (CNN) has been used in the project to ease the process of neural learning. The data set used were retrieved from MESSIDOR database and it contains 1200 pieces of fundus images. The images were filtered based on the project needed.  There were 580 pieces of images types .tif has been used after filtered and those pictures were divided into 2, which is Exudates images and Normal images. On the training and testing session, the 580 mixed of exudates and normal fundus images were divided into 2 sets which is training set and testing set. The result of the training and testing set were merged into a confusion matrix. The result for this project shows that the accuracy of the CNN for training and testing set was 99.3% and 88.3% respectively.

     

     


     

  • Keywords


    Diabetic Retinopathy; Alexnet Convolution Neural Networks; MESSIDOR database; Deep learning.

  • References


      [1] Feisul, M. I., & Azmi, S. (2013). National diabetes registry report. 2009-2012. Ministry of Health Malaysia.

      [2] Neuwirth, J. (1988). Diabetic retinopathy: What you should know. https://nei.nih.gov/sites/default/files/Diabetic-Retinopathy-What-You-Should-Know-508.pdf.

      [3] Vislisel, J., & Oetting, T. (2010). Diabetic retinopathy: From one medical student to another. http://eyerounds.org/tutorials/diabetic-retinopathy-med-students/Diabetic-Retinopathy-medical-students.pdf.

      [4] Diabetes UK. (2006). What is diabetes. http://www.godnaturalcures.com/healthc/PDF%207%20EBOOKS/natural-help-for-diabetes.pdf.

      [5] Antonetti, D., Klein, R., & Gardener, T., Diabetic retinopathy. (2012). https://www.aoa.org/patients-and-public/eye-and-vision-problems/glossary-of-eye-and-vision-conditions/diabetic-retinopathy.

      [6] Ophthalmic Photographers' Society. Fundus photography overview. https://www.opsweb.org/page/fundusphotography.

      [7] Poornima, S. V., Nishchala, T. K., & Umamakeswari, A. (2014). Detection of diabetic retinopathy by applying total variation. Biomedical Research, 25(4), 560–563.

      [8] Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., P. Gain, R. Ordonez, P. Massin, A. Erginay, & Charton, B. (2014). Feedback on a publicly distributed image database: The Messidor database. Image Analysis and Stereology, 33(3), 231-234.


 

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Article ID: 20804
 
DOI: 10.14419/ijet.v7i4.11.20804




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