Comparative Analysis of Pre-trained ResNet and DenseNet Models for the Detection of Diabetic Macular Edema

Abstract

A major setback in Deep Learning (DL) is that a huge amount of data is essential to render the trained model more generalizable. Constructing a higher-order model based on insufficient data has a detrimental effect on testing performance. Transfer Learning (TL) involves less training data than conventional DL approaches and offers superior decision support. Healthcare datasets of reasonable sizes are generally inappropriate for training DL models. A promising solution to the issue would be to use TL methods for the classification of medical image datasets. This paper aims at the training and evaluation of six variants of pre-trained ResNet and three variants of pre-trained DenseNet models for Diabetic Macular Edema (DME) classification employing a public retinal Optical Coherence Tomography (OCT) image dataset. Among the ResNet implementations, ResNet101V2 has delivered the highest accuracy value of 95%. And among the DenseNet implementations, DenseNet201 has yielded an exceptional classification accuracy of 99%. As all three DenseNet versions, along with the ResNet101V2 version, have produced noticeably good results (accuracy values greater than 95%), they can be used to screen the retinal OCT images of DME patients.