Implementation of FBSE-EWT method in memristive crossbar array framework for automated glaucoma diagnosis from fundus images

Ocular disorders affect over 2.2 billion people globally, with glaucoma being a leading cause of blindness in India. Early detection of glaucoma is crucial as it gradually damages the optic nerve due to increased fluid pressure, leading to vision impairment. This study introduces an innovative approach for glaucoma detection and diagnosis, utilizing two-dimensional Fourier-Bessel series expansion-based empirical wavelet transforms (2D-FBSE-EWT) combined with a memristive crossbar array (MCA) model. The proposed method leverages deep learning and an ensemble EfficientNetb0 based technique to classify fundus images as either normal or glaucomatous. EfficientNetb0 outperforms compared to other convolutional neural networks (CNNs) such as ResNet50, AlexNet, and GoogleNet, making it the optimal choice for glaucoma classification. Initially, the dataset was processed using the integrated MCA with 2D-FBSE-EWT model, and the reconstructed images were used for further classification. The assessment parameters of the reconstructed images demonstrated high quality, with peak signal-to-noise ratio (PSNR) of 26.2346 dB and structural similarity index (SSIM) of 95.38 %. The proposed method achieved an impressive accuracy of 94.15 % using EfficientNetb0. Additionally, it enhanced accuracy and sensitivity by 32.14 % and 40.93 %, respectively, compared to the unprocessed dataset.