Leveraging Mask Autoencoder and Crossover Binary Sand Cat Algorithm for Early Detection of Glaucoma

Glaucoma, a leading cause of irreversible blindness worldwide, can be effectively managed if detected early. Glaucoma is directly associated with aging as it commonly occurs in people over the age of 40 and in elderly people. Glaucoma detection in retinal fundus images typically involves utilizing image processing and machine learning techniques. By leveraging advancements in computer vision, a robust and automated system is developed to assist ophthalmologists in screening and diagnosing glaucoma from retinal fundus images. Furthermore, fundus images can vary significantly in quality due to factors like illumination variations, focus, and artifacts. Ensuring consistent image quality across different datasets and acquisition devices is essential for reliable detection. Addressing these challenges requires interdisciplinary collaboration between ophthalmologists to develop robust and reliable solutions for the detection of glaucoma. Hence a novel mask autoencoder-based crossover binary sand cat (MA-CBSC) algorithm is proposed to detect glaucoma. In this algorithm, the mask autoencoder recognizes the features indicating the presence of glaucoma in the input images and the crossover binary sand cat algorithm is used to fine tune the overall performance of the algorithm by selecting the most appropriate features escaped due to overfitting issues. Preprocessing steps such as image enhancement, filtering, and data cleaning are applied to the extracted ROI for the purpose of increasing the image quality and enhancing the visibility of features relevant to glaucoma detection. ROI extraction attributes namely optic disc, cup-to-disc ratio, bean-pot cupping, and vertical enlargement are derived from the ROI along with some other relevant features. In this work, the crossover-based binary sand cat optimization algorithm is utilized for hyperparameter tuning to enhance the efficiency of the MA-CBSC method. Extensive experimental assessments are conducted, comparing the effectiveness of MA-CBSC algorithms with the Retinal Disease Classification dataset, Fundus Glaucoma Detection Data Dataset, and Glaucoma Dataset. The results obtained by the proposed method are compared with the results obtained by the existing techniques such as DLCNN-MGWO-VW, FRCNN-FKM, ML-DCNN, and DNN-MSO to show its superiority. Seven evaluation parameters are used in assessing the efficiency of the developed model including positive predictive value (PPV), accuracy, precision, F1 score, sensitivity, recall, and specificity. These evaluation measures show that the model has a more promising performance than the existing methods with 98.3% accuracy.