Towards the Localisation of Lesions in Diabetic Retinopathy

by   Samuel Ofosu Mensah, et al.

Convolutional Neural Networks (CNN) has successfully been used to classify diabetic retinopathy (DR) fundus images in recent times. However, deeper representations in CNN only capture higher-level semantics at the expense of losing spatial information. To make predictions very usable for ophthalmologists, we use a post-attention technique called Gradient-weighted Class Activation Mapping (Grad-CAM) on the penultimate layer of deep learning models to produce coarse localisation maps on DR fundus images. This is to help identify discriminative regions in the images, consequently providing enough evidence for ophthalmologists to make a diagnosis and saving lives by early diagnosis. Specifically, this study uses pre-trained weights from four (4) state-of-the-art deep learning models to produce and compare the localisation maps of DR fundus images. The models used include VGG16, ResNet50, InceptionV3, and InceptionResNetV2. We find that InceptionV3 achieves the best performance with a test classification accuracy of 96.07 faster than the other models.



There are no comments yet.


page 6


A deep learning model for classification of diabetic retinopathy in eye fundus images based on retinal lesion detection

Diabetic retinopathy (DR) is the result of a complication of diabetes af...

Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation

We proposed a deep learning method for interpretable diabetic retinopath...

Diabetic Retinopathy Screening Using Custom-Designed Convolutional Neural Network

The prevalence of diabetic retinopathy (DR) has reached 34.6 is a major ...

Guided Layer-wise Learning for Deep Models using Side Information

Training of deep models for classification tasks is hindered by local mi...

Doctor of Crosswise: Reducing Over-parametrization in Neural Networks

Dr. of Crosswise proposes a new architecture to reduce over-parametrizat...

Explainable Diabetic Retinopathy Detection and Retinal Image Generation

Though deep learning has shown successful performance in classifying the...

DRDr: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learning

This paper addresses the problem of identifying two main types of lesion...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.