Detection of Diabetic Retinopathy using CNN

@article{1702295,
      author = {U. Satish, S K. Abbddularafath, V. Rishindra, Y. Mukhesh, V. Sai Harsha},
      title = {Detection of Diabetic Retinopathy using CNN},
      journal = {Iconic Research And Engineering Journals},
      year = {2020},
      volume = {3},
      number = {11},
      pages = {86-92},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/17022951.pdf},
      abstract = {The diagnosis the diagnosis of diabetic retinopathy (DR) through color fundus images requires experienced clinicians to identify the presence and significance of many small features which, along with a complex grading system, makes this a difficult and time-consuming task. In this paper, we propose a CNN approach to diagnosing DR from digital fundus images and accurately classifying its severity. We develop a network with CNN architecture and data augmentation which can identify the intricate features involved in the classification task such as micro-aneurysms, exudate and hemorrhage on the retina and consequently provide a diagnosis automatically and without user input. We train this network using a high-end graphics processor unit (GPU) on the publicly available Kaggle dataset and demonstrate impressive results, particularly for a high-level classification task. On the data set of 80,000 images used our proposed CNN achieves a sensitivity of 95% and an accuracy of 75% on 5,000 validation images.},
      keywords = {Deep Learning; Convolution Neural Networks; Diabetic Retinopathy; Image Classification; Diabetes},
      month = {May},
  }