Optimized Improved Random Forest-Fostered Glaucoma Detection from Fundus Retinal Images

Glaucoma is a major cause of irreversible blindness caused by optic nerve damage. The ophthalmologist uses retinal examination of the dilated pupil to diagnose this disease. Since this diagnosis is a manual and laborious procedure, an automated technique is required for faster diagnosis. Automated retinal image processing is deemed a competitive research field owing to its lower accuracy results, complication and improper effects related with it. Therefore, Optimized Improved Random Forest fostered Glaucoma Detection from Fundus Retinal Images (IRF-MOSOA-GD) is proposed in this paper. Here, Images are acquired through the datasets of DRISHTI-GS, ORIGA and RIM_ONE and given to the pre-processing. The pre-processing is carried out utilizing the Savitzky-Golay Denoising technique for eliminating the noise at the input images. Then the pre-processed image is given to the feature extraction phase. In the feature extraction phase, the region features are extracted with the help of the Fuzzy color and Texture histogram (FCTH), Edge histogram and Pyramid Histograms of Orientation Gradients (PHOG) method. Then, the extracted feature is fed to the Improved Random Forest (IRF) classifier for categorizing the normal and Glaucoma images. The hyperparameter of the IRF classifier is tuned with a Multi-Objective Squirrel Optimization Algorithm (MOSOA) to attain better categorization of normal and glaucoma images. The proposed technique is implemented in Java and its efficiency is analyzed under some metrics, like accuracy, F-scores and computational time. The IRF-MOSOA-GD method attains higher accuracy in the DRISHTI-GS dataset at 23.6%, 27.55% and 24.98% higher accuracy compared with existing techniques.