Introducing Feature Attention Module on Convolutional Neural Network for
Diabetic Retinopathy Detection
- URL: http://arxiv.org/abs/2308.02985v1
- Date: Sun, 6 Aug 2023 01:52:46 GMT
- Title: Introducing Feature Attention Module on Convolutional Neural Network for
Diabetic Retinopathy Detection
- Authors: Susmita Ghosh and Abhiroop Chatterjee
- Abstract summary: We propose a new methodology that integrates a feature attention module with a pretrained VGG19 convolutional neural network (CNN) for more accurate DR detection.
The proposed module aims to leverage the complementary information from various regions of fundus images to enhance the discriminative power of the CNN.
- Score: 0.7614628596146599
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Diabetic retinopathy (DR) is a leading cause of blindness among diabetic
patients. Deep learning models have shown promising results in automating the
detection of DR. In the present work, we propose a new methodology that
integrates a feature attention module with a pretrained VGG19 convolutional
neural network (CNN) for more accurate DR detection. Here, the pretrained net
is fine-tuned with the proposed feature attention block. The proposed module
aims to leverage the complementary information from various regions of fundus
images to enhance the discriminative power of the CNN. The said feature
attention module incorporates an attention mechanism which selectively
highlights salient features from images and fuses them with the original input.
The simultaneous learning of attention weights for the features and thereupon
the combination of attention-modulated features within the feature attention
block facilitates the network's ability to focus on relevant information while
reducing the impact of noisy or irrelevant features. Performance of the
proposed method has been evaluated on a widely used dataset for diabetic
retinopathy classification e.g., the APTOS (Asia Pacific Tele-Ophthalmology
Society) DR Dataset. Results are compared with/without attention module, as
well as with other state-of-the-art approaches. Results confirm that the
introduction of the fusion module (fusing of feature attention module with CNN)
improves the accuracy of DR detection achieving an accuracy of 95.70%.
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