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Deep Learning-Based Conjunctival Melanoma Detection Using Ocular Surface Images

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Deep Learning Applications in Image Analysis

Part of the book series: Studies in Big Data ((SBD,volume 129))

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Abstract

The human eye could be affected with conjunctival melanoma, which indicates a fatal malignant growth of the eye. Being a very rare disease, there exists a lack of related data in the literature. Also, very few studies performed deep learning-based conjunctival melanoma detection from the ocular surface images. In response to the research gap, we created a more enriched and well-curated dataset validated by medical experts for conjunctival melanoma detection using deep learning. Furthermore, we have made our dataset available on Kaggle. In the present work, we utilized a total of seven pretrained deep learning-based classification models, that are, DenseNet161, DenseNet201, EfficientNet_B7, GoogLeNet, ResNet18, ResNet50, and ResNet152. We outperformed the prior literature in terms of assessment metrics, including different performance parameters like accuracy, precision, sensitivity, F1-score, specificity, confusion matrix, ROCcurve, and AUROC, with better improvement achieved in multi-label classification. The best AUROC and overall accuracy were found to be around 1.00 and 99.51%, respectively, for binary classification whereas these were around 0.99 and 94.42%, respectively, in case of multi-label classification, from the best performing model, EfficientNet_B7. The deep learning applications in medical images require visual interpretation of the features prioritized in decision-making, and a Grad-CAM-based visualization is added in this study to prove the effectiveness of the best-performing model. The research conducted here may make it simpler to identify conjunctival lesions using state-of-the-art deep learning models more meticulously in the future.

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Funding

This work was made possible by Qatar National Research Fund (QNRF) NPRP12S-0227–190164 and International Research Collaboration Co-Fund (IRCC) grant: IRCC-2021–001. The statements made herein are solely the responsibility of the authors.

Author information

Authors and Affiliations

  1. Department of Biomedical Physics and Technology, University of Dhaka, Dhaka, 1000, Bangladesh

    Kanchon Kanti Podder

  2. Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, 43600, Bangi, Malaysia

    Mohammad Kaosar Alam & Zakaria Shams Siam

  3. Department of Electrical and Computer Engineering, Presidency University, Dhaka, Bangladesh

    Zakaria Shams Siam

  4. Department of Electrical Engineering, Qatar University, 2713, Doha, Qatar

    Khandaker Reajul Islam, Amith Khandakar & Muhammad E. H. Chowdhury

  5. Department of Electrical and Electronic Engineering, Chittagong University of Engineering and Technology, Chittagong, 4349, Bangladesh

    Proma Dutta

  6. Clinical Imaging Department, Hamad Medical Corporation, Doha, Qatar

    Adam Mushtak

  7. Department of Basic Medical Sciences, College of Medicine, Qatar University, 2713, Doha, Qatar

    Shona Pedersen

Authors
  1. Kanchon Kanti Podder
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  2. Mohammad Kaosar Alam
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  9. Muhammad E. H. Chowdhury
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Corresponding author

Correspondence to Muhammad E. H. Chowdhury .

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Editors and Affiliations

  1. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

    Sanjiban Sekhar Roy

  2. College of Information and Electrical Engineering, Asia University, Musashino, Taiwan

    Ching-Hsien Hsu

  3. Department of Computer Science And Engineering, National Institute of Technology Warangal, Warangal, India

    Venkateshwara Kagita

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Podder, K.K. et al. (2023). Deep Learning-Based Conjunctival Melanoma Detection Using Ocular Surface Images. In: Roy, S.S., Hsu, CH., Kagita, V. (eds) Deep Learning Applications in Image Analysis. Studies in Big Data, vol 129. Springer, Singapore. https://doi.org/10.1007/978-981-99-3784-4_6

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