Multi-Scale Feature Pyramid for Detection of Red Lesions in Fundus Images
Goutam Kumar Ghorai1, Swagata Kundu2, Gautam Sarkar3, Ashis Kumar Dhara4
1Goutam Kumar Ghorai, Department of Electrical Engineering, Jadavpur University, Kolkata (West Bengal), India.
2Swagata Kundu, Department of Electrical Engineering, National Institute of Technology Durgapur, Durgapur (West Bengal), India.
3Gautam Sarkar, Department of Electrical Engineering, Jadavpur University, Kolkata (West Bengal), India.
4Ashis Kumar Dhara, Department of Electrical Engineering, National Institute of Technology Durgapur, Durgapur (West Bengal), India.
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 | PP: 14-19 | Volume-12 Issue-4, November 2023 | Retrieval Number: 100.1/ijrte.D79511112423 | DOI: 10.35940/ijrte.D7951.1112423
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Diabetic Retinopathy, Fundus Images, Red Lesions Detection, Feature Pyramid Network, Focal loss
Scope of the Article: Artificial Intelligence