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Logarithmic Progressive-SMOTE: Oversampling Minorities in Retinal Fundus Multi-disease Image Dataset

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Computer Vision and Image Processing (CVIP 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1776))

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Abstract

Multiple retinal diseases co-occur more frequently. It is extremely challenging to effectively diagnose several diseases in an image and provide appropriate treatment for those diseases with high accuracy. The performance of such an imbalanced dataset, which has minority classes, can be enhanced. The Retinal Fundus Multi-Disease Image Dataset (RFMiD) is a collection of 3200 Multi-labeled Imbalanced Dataset (MLID) marked with 46 different disease labels. The suggested Logarithmic Progressive Synthetic Minority Oversampling Technique (LP-SMOTE) is intended to oversample the minority classes in imbalanced dataset. The Imbalance Ratio Per Label (IRPL) and Mean Imbalance Ratio (MeanIR) are used and assessed to distinguish the minority and majority classes. The logarithmic progressive sampling per label is applied to minority classes. The Oversampling is implemented to level up the samples in minority classes. The proposed technique improved the overall minority class samples by 5.4 times. After employing LP-SMOTE technique, the overall validation accuracy and test accuracy is improved by 2.81% and 4.53% respectively on VGG19 pre-trained model.

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Acknowledgements

We extend out thanks Technical Education Quality Improvement Program (TEQIP), the World Bank project, for providing a state of the art Center of Excellence in Signal and Image Processing research lab. Also, we thank the authors of “Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research” for making their dataset RFMiD publicly available.

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Authors and Affiliations

  1. Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, Maharashtra, India

    Sachin Panchal & Manesh Kokare

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  1. Sachin Panchal
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  2. Manesh Kokare
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Correspondence to Sachin Panchal .

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Editors and Affiliations

  1. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Deep Gupta

  2. Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Kishor Bhurchandi

  3. Indian Institute of Technology Ropar, Rupnagar, India

    Subrahmanyam Murala

  4. Indian Institute of Technology Roorkee, Roorkee, India

    Balasubramanian Raman

  5. Indian Institute of Technology Roorkee, Roorkee, India

    Sanjeev Kumar

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Panchal, S., Kokare, M. (2023). Logarithmic Progressive-SMOTE: Oversampling Minorities in Retinal Fundus Multi-disease Image Dataset. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1776. Springer, Cham. https://doi.org/10.1007/978-3-031-31407-0_29

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  • DOI: https://doi.org/10.1007/978-3-031-31407-0_29

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  • Online ISBN: 978-3-031-31407-0

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