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Classifying diabetic macular edema grades using extended power of deep learning

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Abstract

Diabetic macular edema (DME) is the expansion of the disease diabetic retinopathy (DR). Diabetic persons with a severe risk of DME can enter the phase of irreversible vision loss. To avoid this stage, patients must undergo identification and treatment twice a year. For identification purposes, this paper introduces a method called Squeeze-and-Excitation embedded DenseNet121 (SEDense) to classify the severity of DME grades. Pre-processing, such as augmentation and green channel extraction, is performed. The augmentation produces 1170 images from the original 413 images to train. The SEDense is assessed using 103 retinal fundus images. SEDense outperformed other state-of-the-art models presented in the “Diabetic Retinopathy - Segmentation and Grading Challenge” at ISBI-2018. It classifies the DME grades with 88.35% accuracy. The proposed SEDense model reduces the hassle of ophthalmologists in diagnosing DME grades.

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Data Availability

IDRiD dataset is available at https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid

Notes

  1. https://ieee-dataport.org/open-access/indian-diabetic-retinopathy-image-dataset-idrid

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  1. Department of Computer Science & Engineering, National Institute of Technology Patna, Ashok Rajpath, Patna, 800005, Bihar, India

    Amit Kumar & Anand Shanker Tewari

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  1. Amit Kumar
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Kumar, A., Tewari, A.S. Classifying diabetic macular edema grades using extended power of deep learning. Multimed Tools Appl 83, 14151–14172 (2024). https://doi.org/10.1007/s11042-023-15746-7

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