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H1DBi-R Net: Hybrid 1D Bidirectional RNN for Efficient Diabetic Retinopathy Detection and Classification

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Abstract

Nowadays the eye disease that widely affects the visual impairment of humans is Diabetes Retinopathy (DR). The advanced stage of the disorder leads to cause complete vision loss and creates complex situations for treatment. So it is significant to treat prolonged diabetes at an initial stage. Therefore, the main reason for DR is the uncontrolled growth of blood glucose levels in the eye. If it reaches the severity level the bleeding is caused in the eye. However, the lesions generated due to DR are medicated based on fundus images. The significant purpose of affecting DR is the presence of high sugar in the blood and this damages the retina. Therefore proper screening of DR is essential to prevent it from affecting the blood vessels all over the body. Also, it unblocks blood vessels paves the way to function the new blood vessels grown in the eye. Therefore a novel hybrid oppositional fire-fly modified 1D bidirectional recurrent (HOF-M1DBR) method is proposed to detect the DR through fundus images accurately. The datasets Messidor-1 and APTOS-2019 are applied for performing the initial process of fundus images such as denoising, smoothing, cropping, and resizing. The M1DBR is used to validate the accuracy and optimize the weight function using the Opposition-Based Learning-FireFly (OBL-FF) algorithm. Thus prediction and classification of f DR from the fundus images are detected accurately and distinguished the four levels from the extracted features. The validation of the proposed method is performed based on accuracy, precision, recall, and F1-score. The experimental results revealed that the proposed HOF-M1DBR attained an accuracy of 98.9% for the Messidor-1 and APTOS-2019 dataset, thus improving the performance. However, the existing DCNN-PCA-FF, DNN-MSO, SI-GWO, and DCNN-EMF methods diminished the performance by 79.9%, 87%, 83%, and 89.1% respectively.

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Data availability

Datasets related to this article can be found at APTOS 2019 and Messidor-1 (https://www.adcis.net/en/third-party/messidor/) and https://www.kaggle.com/c/aptos2019-blindness-detection/overview/aptos-2019, an open-source online data repository.

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Authors and Affiliations

  1. Zhejiang Bioinformatics International Science and Technology Cooperation Center, Wenzhou-Kean University, Wenzhou, 325060, China

    Sujatha Krishnamoorthy

  2. Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics, Wenzhou Kean University, Wenzhou, 325060, China

    Sujatha Krishnamoorthy

  3. College of Science Mathematics and Technology, Wenzhou Kean University, Wenzhou, 325060, China

    Sujatha Krishnamoorthy

  4. Research scholar, Zhejiang, China

    Yu Weifeng

  5. Department of Applied Data Science, Noroff University College, Kristiansand, Norway

    Seifedine Kardy

  6. Artificial Intelligence Research Center (AIRC), Ajman University, 346, Ajman, United Arab Emirates

    Jingling Luo & Seifedine Kardy

  7. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

    Seifedine Kardy

Authors
  1. Sujatha Krishnamoorthy
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  2. Yu Weifeng
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  3. Jingling Luo
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  4. Seifedine Kardy
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Contributions

All agreed on the content of the study. SK, YW, JL, and collected all the data for analysis from different data sources. SK, and SK agreed on the methodology. SK, and SK completed the analysis based on the agreed steps. Results and conclusions are discussed and written together. All authors read and approved the final manuscript.

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Correspondence to Sujatha Krishnamoorthy.

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Krishnamoorthy, S., Weifeng, Y., Luo, J. et al. H1DBi-R Net: Hybrid 1D Bidirectional RNN for Efficient Diabetic Retinopathy Detection and Classification. Artif Intell Rev 56 (Suppl 2), 2759–2787 (2023). https://doi.org/10.1007/s10462-023-10589-y

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