Efficient retinal detachment classification using hybrid machine learning with levy flight-based optimization

Decreased vision acuity and blindness due to retinal detachment (RD) are significant concerns, emphasizing the importance of early diagnosis and identification. However, manual screening of RD is labor-intensive and timeconsuming and faces challenges such as poor quality and low accuracy. A novel hybrid machine learning(ML) algorithm incorporating Levy flight-based atom search optimization (LFB-ASO) is proposed to solve the above challenges. The dataset utilized for the experiment is the Retinal Fundus Multi-disease Image dataset (RFMiD). The data preprocessing pipeline involves image resizing, normalization, data augmentation, masking and segmentation. To ensure consistent dimensions, all retinal images are standardized through resizing. Performance and convergence are improved using normalization. The data augmentation technique enhances diversity, while segmentation focuses on the region of interest (ROI). Then the deep features are extracted from the preprocessed retinal images using a pre-trained ResNet18 model. LFB-ASO is employed to select the most discriminative deep features for RD classification. To achieve superior accuracy, hybrid ML algorithms, namely Support Vector Machine (SVM), Gradient Boosting Machine (GBM) and Random Forest (RF) are employed. The proposed model achieves remarkable results with accuracy, recall, F1 score and precision of 98.75%, 96.70%, 97.01% and 97.62%. These results outperform existing methods such as HOS-LSDA, LR, NB, PCA and RD-Light-Net.