Title: Fractional Archimedes optimisation algorithm enabled deep learning for diabetic retinopathy detection

Authors: Baddur Kundan; Pushpa Sangaralingam

Addresses: Department of Computer Science and Engineering, St. Peter's Institute of Higher Education and Research, Avadi, Chennai, Tamil Nadu – 600054, India ' Department of Computer Science and Engineering, St. Peter's Institute of Higher Education and Research, Avadi, Chennai, Tamil Nadu – 600054, India

Abstract: This paper proposes a fractional Archimedes optimisation algorithm (FAOA)-based deep learning model for diabetic retinopathy (DR) detection. Here, the FAOA is formed by combining fractional calculus with Archimedes optimisation algorithm (AOA). Initially, in the pre-processing step, the noise from the input image is removed by the Kalman filter and region of interest (RoI) is extracted. Then, optic disc segmentation and blood vessel segmentation are done. After that, the features are extracted from both segmented outputs. Simultaneously, features are taken from the Haar wavelet transform, which is derived from the input image. Then, extracted features are allowed to FAOA_LeNet for DR detection. The performance is analysed using the Indian diabetic retinopathy image dataset and digital retinal images for vessel extraction dataset. The performance obtained by the proposed FAOA_LeNet in terms of sensitivity, accuracy, and specificity is 0.938, 0.954, and 0.974.

Keywords: Kalman filter; Archimedes optimisation algorithm; AOA; fuzzy C-means clustering; fractional calculus; Haar wavelet transform; HWT; region of interest; RoI.

DOI: 10.1504/IJAHUC.2025.146125

International Journal of Ad Hoc and Ubiquitous Computing, 2025 Vol.49 No.1, pp.1 - 20

Received: 12 Jul 2023
Accepted: 20 Aug 2024
Published online: 07 May 2025 *

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