Transfer learning-based platform for detecting multi-classification retinal disorders using optical coherence tomography images

One of the primary concerns of computer-aided diagnosis is the detection of retinal disorders. The study aims to categorize the patients into choroidal neovascularization, diabetic macular edema, drusen, and normal by using optical coherence tomography (OCT) images. For the first time, two novel transfer learning-based techniques were used for retinal disorder classification: SqueezeNet and the Inception V3 Net. Two SqueezeNet scenarios were used to compare the performance of the original SqueezeNet and the improved one. A dataset of 11 200 OCT images was used for data partitioning of SqueezeNet and, meanwhile, 18 000 images for Inception V3 Net. The modified SqueezeNet achieved 98% accuracy, a 1.2% improvement over the original. The Inception V3 Net classifier improved its classification accuracy to 98.4%. When compared to other classifiers and a human expert, the transfer learning approach demonstrated its robustness in the challenge of retinal disorders classification with a large dataset.