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An Efficient System for Grading Diabetic Retinopathy by Detecting the Location of Lesions

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Artificial Intelligence and Sustainable Computing (ICSISCET 2022)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Nowadays, diabetic retinopathy, also called as diabetic eye disease, has become a major cause of vision loss. In modern ophthalmology, an accurate detection and grading of diabetic retinopathy using the color retinal fundus photograph remains a challenging task. This proposed work presents an efficient system to detect exudates that will aid the accurate grading of diabetic retinopathy. For this purpose, a novel two-stage hierarchical classifier is introduced to differentiate the pathological and healthy retinal images. Initially, the green channel of each RGB retinal image is preprocessed and the exudates are detected. Next to analyze and discriminate the normal and lesion-affected images, a set of feature vectors based on size, texture, and intensity are extracted. Then, these features are classified using a two-stage hierarchical classifier. Finally, based on this classification result, an automatic grading system for the detection of diabetic retinopathy is developed and validated on five publicly available databases. The proposed grading system achieves 100% sensitivity, 98.24% specificity, and 0.95 AUC, which is high, compared to other state-of-art methods.

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Author information

Authors and Affiliations

  1. SRM TRP Engineering College, Tiruchirappalli, Tamil Nadu, India

    B. Ramasubramanian & M. Muthu Manjula

  2. S.A. Engineering College, Chennai, Tamil Nadu, India

    D. Hemanand

  3. Kongu Engineering College, Erode, Tamil Nadu, India

    K. Kavinkumar

Authors
  1. B. Ramasubramanian
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  2. D. Hemanand
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  3. K. Kavinkumar
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  4. M. Muthu Manjula
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Corresponding author

Correspondence to B. Ramasubramanian .

Editor information

Editors and Affiliations

  1. Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India

    Manjaree Pandit

  2. Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India

    M. K. Gaur

  3. CHRIST (Deemed to be University), Kengeri Campus, Bengaluru, India

    Sandeep Kumar

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Ramasubramanian, B., Hemanand, D., Kavinkumar, K., Muthu Manjula, M. (2023). An Efficient System for Grading Diabetic Retinopathy by Detecting the Location of Lesions. In: Pandit, M., Gaur, M.K., Kumar, S. (eds) Artificial Intelligence and Sustainable Computing. ICSISCET 2022. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-1431-9_57

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