Collaboration of features optimization techniques for the effective diagnosis of glaucoma in retinal fundus images.

• We 65 extracted structural and non-structural feature sets for glaucoma prediction. • Our selected three nature-computing algorithms and two novel algorithms have been applied first time to these glaucoma datasets in combination with these characteristics. As a result, the results from this series of experiments are entirely novel. • The performance test of Bat Algorithm and Binary Cuckoo Search on two typical publicly accessible datasets demonstrates that they are highly efficient in glaucoma prediction. Extensive testing and analysis were conducted (3 machine learning and 5 soft computing techniques were tested on three combinations of two datasets). • This work adds to scientific research by suggesting a unique classifier strategy that achieves astonishing results, up to 98.95% accuracy in detecting glaucoma illness in the binary class. The suggested system's categorization accuracy is evaluated against two widely used and publicly accessible datasets. • This work provides an automated primary glaucoma screening system based on quantitative analysis of fundus pictures to aid ophthalmologists in detecting glaucoma disease more quickly and affordably. It could also be used as a second opinion. With this system, human race has been equipped with a low-cost, rapid, robust, readily implementable, and reliable intelligence system for screening this infection. As a result, the pace of eyesight loss may be minimized. Glaucoma is the second most common cause of vision loss. Manual screening of a patient's eye or screening through a fundus image of the patient's eye requires expert ophthalmologists. This screening analysis is time-consuming, requires expert human involvement, and is subject to human intra-observer variability. Thus, medical imaging professionals are working to solve these issues by investigating retinal images for glaucoma detection using artificial intelligence-based computer-aided diagnosis systems (CAD). Machine learning algorithms (for classification) and nature-inspired computing (for feature selection/reduction) embedded CAD systems can successfully identify retinal pictures and can be employed to overcome these challenges. This proposed work is a productive attempt in which we have proposed two novel two-layered approaches (BA-BCS, BCS-PSO) which are based on Particle Swarm Optimization (PSO), Binary Cuckoo Search (BCS), and Bat Algorithm (BA). We have also analyzed the performances of BA, BCS, and PSO separately. These five (single and two-layered) approaches are used to compose subsets of reduced features that can generate the maximum accuracy when forwarded to three machine learning classifiers. Benchmark publicly available datasets, ORIGA and REFUGE, and their combinations are used to validate the proposed methodology. A maximum accuracy of up to 98.95% is achieved using these approaches. Apart from this, many other trade-off solutions are also suggested for the researcher's community. This study therefore presents novel efforts with new and efficient results that are beneficial to ophthalmologists, researchers, and humanity. [ABSTRACT FROM AUTHOR]