Age‐Related Macular Degeneration Detection Using OCT Image: Deep Hybrid Classifier With Improved PCA ‐Based Selective Feature Set

Age‐related macular degeneration (AMD) is a progressive retinal disease that can lead to vision loss if not diagnosed early. Accurate and timely detection is essential for effective treatment. This study presents a comprehensive deep learning‐based framework for the automated detection of AMD using optical coherence tomography (OCT) images. The proposed method integrates advanced preprocessing, segmentation, feature extraction, and classification techniques. An improved Wiener filter is applied to enhance image quality by reducing noise while preserving edge details. A U‐Net architecture is then used for accurate segmentation of retinal regions. Subsequently, diverse features such as histogram of oriented gradients (HOG), gray‐level co‐occurrence matrix (GLCM), scale‐invariant feature transform (SIFT), VGG16, statistical features, and improved median ternary pattern (MTP) are extracted from segmented images. Then, an enhanced principal component analysis (PCA) with feature scaling is used for the optimal selection of features to improve classification robustness. Finally, a hybrid model combining deep convolutional neural networks (DCNNs) and AlexNet is proposed for AMD classification. Moreover, the proposed model is evaluated against traditional methods and achieves superior performance, with an accuracy of 0.958 and F ‐measure of 0.964. Experimental results confirm the model's effectiveness and reliability, highlighting its potential for clinical application in early AMD detection and diagnosis.