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Optical system based data classification for diabetes retinopathy detection using machine language with artificial intelligence

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Abstract

Diabetes causes DR. Diabetes duration influences retinopathy development. The retinal vein weakening may have no side effects or a little eyesight impairment at initially. Blindness may occur. DR intervention and treatment need early clinical indications. Thus, frequent eye examinations must guide patients to a doctor for a full eye inspection and therapy to avoid irreversible vision loss. This work develops a machine learning-based optical image-based data classification method for diabetic retinopathy identification. OCT analyses the retinal picture and the ensemble pulse coupled filtering and green histogram channel equalization-based adaptive filtering segment this picture for blood vessel characterization. CenterResnet-50 classifies images for color fundus detection. Classification accuracy, sensitivity, specificity, AUC, and ROC curves were examined for various optical retina pictures. The proposed method has 98% classification accuracy, 67% sensitivity, 73% specificity, and 63% AUC.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, IIMT University, Meerut, Uttar Pradesh, India

    Suraj Malik

  2. Department of Computer Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

    S. Srinivasan

  3. Department of Electronics and Communication Engineering, School of Engineering and Technology, Jaipur National University, Jaipur, India

    Chandra Shekhar Rajora

  4. Department of Management, Sanskriti University, Mathura, Uttar Pradesh, India

    Sachin Gupta

  5. Department of Computer Science and Engineering, Presidency University, Bengaluru, India

    Mohammed Mujeer Ulla

  6. Department of Electronics and Communication Engineering, Teerthanker Mahaveer University, Bagadpur, Uttar Pradesh, India

    Neeraj Kaushik

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  1. Suraj Malik
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  2. S. Srinivasan
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  3. Chandra Shekhar Rajora
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  4. Sachin Gupta
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  5. Mohammed Mujeer Ulla
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  6. Neeraj Kaushik
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Contributions

SM: Conceived and design the analysis. Writing- Original draft preparation. SS: Collecting the Data, CSR: Contributed data and analysis stools, SG: Performed and analysis, MMU: Performed and analysis, NK: Wrote the Paper. Editing and Figure Design.

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Correspondence to Suraj Malik.

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Malik, S., Srinivasan, S., Rajora, C.S. et al. Optical system based data classification for diabetes retinopathy detection using machine language with artificial intelligence. Opt Quant Electron 55, 896 (2023). https://doi.org/10.1007/s11082-023-05193-x

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