Published November 30, 2023 | Version CC-BY-NC-ND 4.0
Journal Open

Multi-Scale Feature Pyramid for Detection of Red Lesions in Fundus Images

Creators

  • 1. Department of Electrical Engineering, Jadavpur University, Kolkata (West Bengal), India.

Contributors

Contact person:

  • 1. Department of Electrical Engineering, Jadavpur University, Kolkata (West Bengal), India.
  • 2. Department of Electrical Engineering, National Institute of Technology Durgapur, Durgapur (West Bengal), India.

Description

Abstract: Diabetic retinopathy (DR) is increasing rapidly around the world, but there is a shortage of experienced ophthalmologists. Therefore, computer-based diagnosis of the fundus images is essential to screening of referable DR. Automated detection of red lesions is very important for screening of DR. This paper deals with a novel method for automatic detection of red lesion. The main contribution is developing a deep learning based detection framework to handle severe class imbalance and imbalance in sizes of red lesions. The multi-scale features are extracted using the feature pyramid network. A pyramid of features is generated with strong semantics. The proposed network is end-to-end trainable in image level with several scales and works for a wide range of red lesions with acceptable performance. Sensitivity of the proposed method is 0.76 with six false-positive per image on test set of publicly available DIARECTDB1 database and outperforms state-of-the-art approaches. A potential benefit with deep learning based detection framework could be used in screening programs of referable DR.

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Additional details

Identifiers

DOI
10.35940/ijrte.D7951.1112423
ISSN
2277-3878

Dates

Accepted
2023-11-15
Manuscript received on 19 October 2023 | Revised Manuscript received on 06 November 2023 | Manuscript Accepted on 15 November 2023 | Manuscript published on 30 November 2023

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