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Diagnosis of retinal damage using Resnet rescaling and support vector machine (Resnet-RS-SVM): a case study from an Indian hospital

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Abstract

Purpose

This study aims to address the challenge of identifying retinal damage in medical applications through a computer-aided diagnosis (CAD) approach. Data was collected from four prominent eye hospitals in India for analysis and model development.

Methods

Data was collected from Silchar Medical College and Hospital (SMCH), Aravind Eye Hospital (Tamil Nadu), LV Prasad Eye Hospital (Hyderabad), and Medanta (Gurugram). A modified version of the ResNet-101 architecture, named ResNet-RS, was utilized for retinal damage identification. In this modified architecture, the last layer's softmax function was replaced with a support vector machine (SVM). The resulting model, termed ResNet-RS-SVM, was trained and evaluated on each hospital's dataset individually and collectively.

Results

The proposed ResNet-RS-SVM model achieved high accuracies across the datasets from the different hospitals: 99.17% for Aravind, 98.53% for LV Prasad, 98.33% for Medanta, and 100% for SMCH. When considering all hospitals collectively, the model attained an accuracy of 97.19%.

Conclusion

The findings demonstrate the effectiveness of the ResNet-RS-SVM model in accurately identifying retinal damage in diverse datasets collected from multiple eye hospitals in India. This approach presents a promising advancement in computer-aided diagnosis for improving the detection and management of retinal diseases.

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Data availability

The datasets generated during and/or analyzed during the current study are available in the IEEE data port. https://ieee-dataport.org/documents/retinal-detachment-dataset

Code availability

The code generated and/or analyzed during the current study is available from the corresponding author upon reasonable request.

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Funding

This study was not supported by any funding.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, VSSUT Burla, Burla, 768018, India

    Santi Kumari Behera

  2. Department of Computer Science and Engineering, ITER, SOA University, Bhubaneswar, Odisha, India

    Rina Mahakud

  3. Department of Electronics and Telecommunication Engineering, Trident Academy of Technology, Bhubaneswar, Odisha, India

    Millee Panigrahi

  4. Department of ECE, Guru Ghasidas Vishwavidyalaya, Bilaspur, C. G., India

    Prabira Kumar Sethy

  5. Department of Basic Science and Humanities, Sambalpur University Institute of Information Technology, Burla, Odisha, India

    Rasmikanta Pati

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  1. Santi Kumari Behera
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  2. Rina Mahakud
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  3. Millee Panigrahi
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  4. Prabira Kumar Sethy
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  5. Rasmikanta Pati
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Contributions

The collaborative efforts of Millee Panigrahi, Rina Mahakud, Santi Kumari Behera, Rasmi Kanta Pati, and Prabira Kumar Sethy have culminated in this research article. Millee Panigrahi played a pivotal role in conceptualization, methodology design, data curation, formal analysis, and the initial drafting of the manuscript. Rina Mahakud contributed significantly to the investigation process, data curation, and provided valuable insights during the writing and editing phases. Santi Kumari Behera's expertise was instrumental in methodological aspects, software implementation, formal analysis, and contributing to the initial draft and visualizations. Rasmi Kanta Pati took charge of conceptualization, resource management, data curation, and played a key role in the critical review and editing of the manuscript. Prabira Kumar Sethy provided overall supervision, participated in the writing and editing process, and ensured the validation of the research outcomes. The final manuscript reflects the combined dedication and diverse skill sets of all authors who have collectively shaped and enriched the research.

Corresponding author

Correspondence to Prabira Kumar Sethy.

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Behera, S.K., Mahakud, R., Panigrahi, M. et al. Diagnosis of retinal damage using Resnet rescaling and support vector machine (Resnet-RS-SVM): a case study from an Indian hospital. Int Ophthalmol 44, 174 (2024). https://doi.org/10.1007/s10792-024-03058-0

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