A Multi-Label Deep Learning Model with Interpretable Grad-CAM for Diabetic Retinopathy Classification

The characteristics of diabetic retinopathy (DR) fundus images generally consist of multiple types of lesions which provided strong evidence for the ophthalmologists to make diagnosis. It is particularly significant to figure out an efficient method to not only accurately classify DR fundus images but also recognize all kinds of lesions on them. In this paper, a deep learning-based multi-label classification model with Gradient-weighted Class Activation Mapping (Grad-CAM) was proposed, which can both make DR classification and automatically locate the regions of different lesions. To reducing laborious annotation work and improve the efficiency of labeling, this paper innovatively considered different types of lesions as different labels for a fundus image so that this paper changed the task of lesion detection into that of image classification. A total of five labels were pre-defined and 3228 fundus images were collected for developing our model. The architecture of deep learning model was designed by ourselves based on ResNet. Through experiments on the test images, this method acquired a sensitive of 93.9% and a specificity of 94.4% on DR classification. Moreover, the corresponding regions of lesions were reasonably outlined on the DR fundus images.

[1]  Adrian Galdran,et al.  A Weakly-Supervised Framework for Interpretable Diabetic Retinopathy Detection on Retinal Images , 2018, IEEE Access.

[2]  Di Xiao,et al.  Exudate detection for diabetic retinopathy with convolutional neural networks , 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[3]  Ronnie D. Caytiles,et al.  Classification of Diabetic Retinopathy Images by Using Deep Learning Models , 2018 .

[4]  Romany F Mansour,et al.  Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy , 2018, Biomedical engineering letters.

[5]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[6]  Santi P. Maity,et al.  Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy , 2018, IEEE Transactions on Biomedical Engineering.

[7]  S. R. Nirmala,et al.  Retinal Image Analysis : A Review , 2011 .

[8]  Muhammad Sharif,et al.  A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions , 2017, J. Comput. Sci..

[9]  Kang Yang,et al.  An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[10]  Mohamed Elhoseny,et al.  An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE , 2019, Optics & Laser Technology.

[11]  Wei Xu,et al.  CNN-RNN: A Unified Framework for Multi-label Image Classification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Taghi M. Khoshgoftaar,et al.  A survey on Image Data Augmentation for Deep Learning , 2019, Journal of Big Data.

[13]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Andrzej Grzybowski,et al.  Review of Diabetic Retinopathy Screening Methods and Programmes Adopted in Different Parts of the World , 2015 .

[15]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[16]  Benzhi Chen,et al.  Weakly Supervised Lesion Detection From Fundus Images , 2019, IEEE Transactions on Medical Imaging.