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CNN-Hyperparameter Optimization for Diabetic Maculopathy Diagnosis in Optical Coherence Tomography and Fundus Retinography

Author

Listed:
  • Ghada Atteia

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Nagwan Abdel Samee

    (Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
    Department of Computer Engineering, College of Engineering, Misr University for Science and Technology (MUST), Giza 12511, Egypt)

  • El-Sayed M. El-Kenawy

    (Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt)

  • Abdelhameed Ibrahim

    (Computer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt)

Abstract

Diabetic Maculopathy (DM) is considered the most common cause of permanent visual impairment in diabetic patients. The absence of clear pathological symptoms of DM hinders the timely diagnosis and treatment of such a critical condition. Early diagnosis of DM is feasible through eye screening technologies. However, manual inspection of retinography images by eye specialists is a time-consuming routine. Therefore, many deep learning-based computer-aided diagnosis systems have been recently developed for the automatic prognosis of DM in retinal images. Manual tuning of deep learning network’s hyperparameters is a common practice in the literature. However, hyperparameter optimization has shown to be promising in improving the performance of deep learning networks in classifying several diseases. This study investigates the impact of using the Bayesian optimization (BO) algorithm on the classification performance of deep learning networks in detecting DM in retinal images. In this research, we propose two new custom Convolutional Neural Network (CNN) models to detect DM in two distinct types of retinal photography; Optical Coherence Tomography (OCT) and fundus retinography datasets. The Bayesian optimization approach is utilized to determine the optimal architectures of the proposed CNNs and optimize their hyperparameters. The findings of this study reveal the effectiveness of using the Bayesian optimization for fine-tuning the model hyperparameters in improving the performance of the proposed CNNs for the classification of diabetic maculopathy in fundus and OCT images. The pre-trained CNN models of AlexNet, VGG16Net, VGG 19Net, GoogleNet, and ResNet-50 are employed to be compared with the proposed CNN-based models. Statistical analyses, based on a one-way analysis of variance (ANOVA) test, receiver operating characteristic (ROC) curve, and histogram, are performed to confirm the performance of the proposed models.

Suggested Citation

  • Ghada Atteia & Nagwan Abdel Samee & El-Sayed M. El-Kenawy & Abdelhameed Ibrahim, 2022. "CNN-Hyperparameter Optimization for Diabetic Maculopathy Diagnosis in Optical Coherence Tomography and Fundus Retinography," Mathematics, MDPI, vol. 10(18), pages 1-30, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3274-:d:910899
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    References listed on IDEAS

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    1. Xiaofan Cheng & Liang Tan & Fangpeng Ming, 2021. "Feature Fusion Based on Convolutional Neural Network for Breast Cancer Auxiliary Diagnosis," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-10, September.
    2. Peter Guttorp & Tilmann Gneiting, 2006. "Studies in the history of probability and statistics XLIX On the Matern correlation family," Biometrika, Biometrika Trust, vol. 93(4), pages 989-995, December.
    3. Prasanna Porwal & Samiksha Pachade & Ravi Kamble & Manesh Kokare & Girish Deshmukh & Vivek Sahasrabuddhe & Fabrice Meriaudeau, 2018. "Indian Diabetic Retinopathy Image Dataset (IDRiD): A Database for Diabetic Retinopathy Screening Research," Data, MDPI, vol. 3(3), pages 1-8, July.
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    1. Hassan, Sharmarke & Dhimish, Mahmoud, 2023. "Enhancing solar photovoltaic modules quality assurance through convolutional neural network-aided automated defect detection," Renewable Energy, Elsevier, vol. 219(P1).

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