Enhancing ocular diseases recognition with domain adaptive framework: leveraging domain confusion

Abstract

Visual health and optimal eyesight hold immense importance in our lives. However, ocular diseases can inflict emotional and financial hardships on patients and families. While various clinical methods exist for diagnosing ocular conditions, early screening of retinal images offers not only a cost-effective approach but also the detection of potential ocular diseases at earlier stages. Simultaneously, many studies have harnessed Convolutional Neural Networks (CNNs) for image recognition, capitalizing on their potential. Nevertheless, the applicability of most networks tends to be limited across different domains. When well-trained models from a domain are applied to another domain, a significant decline in accuracy might occur, thereby constraining the networks’ practical implementation and wider adoption. In this research endeavor, we present a domain adaptive framework, ResNet-50 with Maximum Mean Discrepancy (RMMD). Initially, we employed ResNet-50 architecture as a foundational network, a popular network used for modification and experimenting with whether a module could improve the accuracy. Additionally, we introduce the concept of Maximum Mean Discrepancy (MMD), a metric for quantifying domain differences. Subsequently, we integrate MMD into the loss function, inducing a state of confusion within the network concerning domain disparities. The outcomes derived from the OIA-ODIR dataset substantiate the efficacy of our proposed network. Our framework attains an impressive accuracy of 40.51% (F1) and 81.06% (AUC, Area Under the Receiver Operating Characteristic Curve), marking a notable enhancement of 9.52% and 7.18% respectively when juxtaposed with the fundamental ResNet-50 model, compared with raw ResNet-50 30.99% (F1) and 73.88% (AUC).