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Optimized quaternion radial Hahn Moments application to deep learning for the classification of diabetic retinopathy

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Abstract

This paper proposes a new hybrid method of classification of fundus images provided by the Asia–Pacific Tele-Ophthalmology Society via combining the discrete moment quaternion approach, the artificial intelligence approach, and machine learning in order to automatically distinguish the stage of diabetic retinopathy using reduced databases divided into five classes. The proposed method is based on two main phases: the preprocessing phase, in which using the new radial invariant moments of Hahn in a quaternion optimized by the ant colony algorithm, in order to calculate the original n × n image moments. The second phase is devoted to introducing the calculated moments into the proposed convolutional neural network model. The present work will contribute to creating new neural network architectures that take advantage of Hahn's new 2D radial moment descriptive capability in quaternions. The K-fold cross-validation method is used to measure the proposed model's performance. Finally, graphical measures such as receiver operating characteristic and precision-rapple curves plus a confusion matrix are presented. Furthermore, numerical measures are adopted for f1-score, loss and precision. In 1795 images, the AUC yielded 94.58%, 97.02%, 94.87%, 97.83%, and 96.54% for the five classes of healthy, mild, moderate, severe, and proliferative respectively. These results prove that the proposed method can be used to detect and classify diabetic retinopathy at an early stage.

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Authors and Affiliations

  1. CED-ST, STIC, Laboratory of Electronic Signals and Systems of Information LESSI, Dhar El Mahrez Faculty of Science, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco

    Mohamed Amine Tahiri & Hassan Qjidaa

  2. Sidi Mohamed Ben Abdellah University, Institute of Sports Sciences, Fez, Morocco

    Hicham Amakdouf

  3. High Normal School, Department of Mathematics and Computer Sciences, Laboratory of ComputerSciences and Interdisciplinary Physics, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Mostafa El mallahi

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Tahiri, M.A., Amakdouf, H., El mallahi, M. et al. Optimized quaternion radial Hahn Moments application to deep learning for the classification of diabetic retinopathy. Multimed Tools Appl 82, 46217–46240 (2023). https://doi.org/10.1007/s11042-023-15582-9

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