Improved detection of dry age-related macular degeneration from optical coherence tomography images using adaptive window based feature extraction and weighted ensemble based classification approach

•Early detection of dry AMD can help to delay disease progression.•Curvature flattening is done for thickness analysis along retinal pigment layer.•A unique adaptive window based feature extraction method is employed.•Weighted majority voting ensemble is offered for disease prediction from OCT image.•Image & patient level validation are done by public dataset with improved accuracy. Dry Age-related macular degeneration (AMD), which affects the older population, can lead to blindness when left untreated. Preventing vision loss in elderly needs early identification. Dry-AMD diagnosis is still time-consuming and very subjective, depending on the ophthalmologist. Setting up a thorough eye-screening system to find Dry-AMD is a very difficult task. This study aims to develop a weighted majority voting (WMV) ensemble-based prediction model to diagnose Dry-AMD. The WMV approach combines the predictions from base-classifiers and chooses the class with greatest vote based on assigned weights to each classifier. A novel feature extraction method is used along the retinal pigment epithelium (RPE) layer, with the number of windows calculated for each picture playing an important part in identifying Dry-AMD/normal images using the WMV methodology. Pre-processing using hybrid-median filter followed by scale-invariant feature transform based segmentation of RPE layer and curvature flattening of retina is employed to measure exact thickness of RPE layer. The proposed model is trained on 70% of the OCT image database (OCTID) and evaluated on remaining OCTID and SD-OCT Noor dataset. Model has achieved accuracy of 96.15% and 96.94%, respectively. The suggested algorithm's effectiveness in Dry-AMD identification is demonstrated by comparison with alternative approaches. Even though the suggested model is only trained on the OCTID, it has performed well when tested on additional dataset. The suggested architecture can be used for quick eye-screening for early identification of Dry-AMD. The recommended method may be applied in real-time since it requires fewer complexity and learning-variables.