Research Area:  Machine Learning

Abstract:

Diabetic retinopathy is one of the major causes of blindness in the population aged 20–65. In this paper, we address the problem of automatic diabetic retinopathy detection and proposed a novel deep learning hybrid to solve the problem. We use transfer learning on pre-trained Inception-ResNet-v2 and added a custom block of CNN layers on top of Inception-ResNet-v2 for building the hybrid model. We evaluated the performance of the proposed model on Messidor-1 diabetic retinopathy dataset and APTOS 2019 blindness detection (Kaggle dataset). Our model performed better than other published results. We achieved a test accuracy of 72.33% and 82.18% on Messidor-1 and APTOS dataset, respectively.

Keywords:  
Diabetic retinopathy
Image classification
Deep learning
Inception ReseNet-v2

Author(s) Name:  Akhilesh Kumar Gangwar & Vadlamani Ravi

Publisher name:  Springer

DOI:  10.1007/978-981-15-5788-0_64

Volume Information:  

Paper Link:   https://link.springer.com/chapter/10.1007/978-981-15-5788-0_64