Clinical Decision Support System for Ophthalmologists for Eye Disease Classification

Abstract

ABSTRACT In medical applications, ocular OCT (optical coherence tomography) is used to assess glaucoma, macular degeneration, diabetic macular edema and other eye diseases as it is capable of showing the cross-sections of tissue layers. The creation of new blood vessels in the choroid layer of the eye is known as choroidal neovascularization (CNV). The aging and macular degeneration will represent the symptom DRUSEN. Our sharp central vision is affected due to DRUSEN. An irreversible vision loss is caused in diabetic patients due to diabetic macular edema (DME). It is mainly due to the leaking of blood vessels in the retina. This research work focus on designing a clinical decision support system to assist the ophthalmologist in classifying the three different types of eye diseases. The existing nine pre-trained CNN models are used for this purpose. The extracted features are used to generate the trained model that is further used for eye disease classification. The training accuracy, validation accuracy, training loss and validation loss are computed for 100 iterations for each pre-trained CNN models during training and validation. The trained model obtained after training is used as input to the classifier, which classifies the images under-diagnosis into NORMAL (normal eye), CNV, DME and DRUSEN. The performance metrics of the classifier designed using each pre-trained models are evaluated and compared for four classes independently. The test results show that the performance of the classifier implemented using the pre-trained model InceptionV3 is better than all other models.