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Designing the Architecture of a Convolutional Neural Network Automatically for Diabetic Retinopathy Diagnosis

Author

Listed:
  • Fahman Saeed

    (Department of Computer Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia)

  • Muhammad Hussain

    (Department of Computer Science, King Saud University, Riyadh 11543, Saudi Arabia)

  • Hatim A. Aboalsamh

    (Department of Computer Science, King Saud University, Riyadh 11543, Saudi Arabia)

  • Fadwa Al Adel

    (Department of Ophthalmology, College of Medicine, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

  • Adi Mohammed Al Owaifeer

    (Ophthalmology Unit, Department of Surgery, College of Medicine, King Faisal University, Al-Ahsa 31982, Saudi Arabia)

Abstract

Diabetic retinopathy (DR) is a leading cause of blindness in middle-aged diabetic patients. Regular screening for DR using fundus imaging aids in detecting complications and delays the progression of the disease. Because manual screening takes time and is subjective, deep learning has been used to help graders. Pre-trained or brute force CNN models are used in existing DR grading CNN-based approaches that are not suited to fundus image complexity. To solve this problem, we present a method for automatically customizing CNN models based on fundus image lesions. It uses k-medoid clustering, principal component analysis (PCA), and inter-class and intra-class variations to determine the CNN model’s depth and width. The designed models are lightweight, adapted to the internal structures of fundus images, and encode the discriminative patterns of DR lesions. The technique is validated on a local dataset from King Saud University Medical City, Saudi Arabia, and two challenging Kaggle datasets: EyePACS and APTOS2019. The auto-designed models outperform well-known pre-trained CNN models such as ResNet152, DenseNet121, and ResNeSt50, as well as Google’s AutoML and Auto-Keras models based on neural architecture search (NAS). The proposed method outperforms current CNN-based DR screening methods. The proposed method can be used in various clinical settings to screen for DR and refer patients to ophthalmologists for further evaluation and treatment.

Suggested Citation

  • Fahman Saeed & Muhammad Hussain & Hatim A. Aboalsamh & Fadwa Al Adel & Adi Mohammed Al Owaifeer, 2023. "Designing the Architecture of a Convolutional Neural Network Automatically for Diabetic Retinopathy Diagnosis," Mathematics, MDPI, vol. 11(2), pages 1-20, January.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:2:p:307-:d:1027855
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    References listed on IDEAS

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    1. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    2. Ren Zhang Tan & XinYing Chew & Khai Wah Khaw, 2021. "Neural Architecture Search for Lightweight Neural Network in Food Recognition," Mathematics, MDPI, vol. 9(11), pages 1-14, May.
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    Cited by:

    1. Yeonwoo Jeong & Jae-Ho Han & Jaeryung Oh, 2023. "Contextual Augmentation Based on Metric-Guided Features for Ocular Axial Length Prediction," Mathematics, MDPI, vol. 11(13), pages 1-20, July.
    2. Xiang Li & Shuo Zhang & Wei Zhang, 2023. "Applied Computing and Artificial Intelligence," Mathematics, MDPI, vol. 11(10), pages 1-4, May.

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