A deep feature fusion and selection-based retinal eye disease detection from OCT images

Optical coherence tomography (OCT) is one of the principal imaging modalities for retinal eye disease detection and classification. Different retinal eye diseases are the leading cause of blindness that can be overcome by early detection. However, ophthalmologists are currently carrying out retinal eye disease detection manually with the help of OCT images that may be erroneous and subjective. Different methods have been presented to automate the manual retinal eye disease detection process that needs further improvement in detection accuracy. This research proposed an automatic method for retinal eye disease detection and classification from OCT images using fusion and selection techniques. First, the modified-Alexnet and ResNet-50 are utilized for deep feature vector extraction. In the next step, these vectors are fused serially and rectified by the proposed feature selection framework and passed as input to different machine learning classifiers for retinal disease diagnosis. For this purpose, a publicly available dataset of retinal eye diseases with four classes is utilized. The proposed retinal eye disease detection method achieved an overall average accuracy index of greater than 99.95%, higher than the top one in the literature, that is, 99.39%. Experimental results authenticated that the proposed retinal eye disease detection methodology can reliably be used for automatic eye disease detection from OCT images. Furthermore, the proposed deep feature and selection-based retinal eye disease detection methodology achieved state-of-the-art performance.