Use of dominant activations obtained by processing OCT images with the CNNs and slime mold method in retinal disease detection

Abstract

Retinal disease is one of the diseases that cause visual symptoms or loss of vision in humans. This disease can affect the choroid, which severely affects vision. Optical coherence tomography (OCT) images are usually used to detect retinal disease. OCT is an imaging technique that takes high-resolution slices of retinal images. It takes time for experts to examine and interpret the OCT images. Experts need to take advantage of technological capabilities to make this process faster and more accurate. Three datasets were used in this study. Dataset #1 (UCSD dataset) consists of choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal OCT image types. Dataset #2 (Duke dataset) and Dataset #3 consist of age-related macular degeneration (AMD), DME, and normal OCT image types. An artificial intelligence based hybrid approach was proposed for retinal disease detection. In the proposed approach, class-based activations were extracted for each model with nine transfer learning models using the dataset. Next, the dominant activations were selected from the model-based activations of each class using the slime mold algorithm (SMA) and the selected activations were classified using the softmax method. The overall accuracy obtained in classification is as follows: 99.60% for dataset 1, 99.89% for dataset #2 and 97.49% for dataset #3. In this study, it was found that the proposed approach contributes to the performance of transfer learning models.