  The prevalence of diabetic retinopathy (DR) has reached 34.6% worldwide and
is a major cause of blindness among middle-aged diabetic patients. Regular DR
screening using fundus photography helps detect its complications and prevent
its progression to advanced levels. As manual screening is time-consuming and
subjective, machine learning (ML) and deep learning (DL) have been employed to
aid graders. However, the existing CNN-based methods use either pre-trained CNN
models or a brute force approach to design new CNN models, which are not
customized to the complexity of fundus images. To overcome this issue, we
introduce an approach for custom-design of CNN models, whose architectures are
adapted to the structural patterns of fundus images and better represent the
DR-relevant features. It takes the leverage of k-medoid clustering, principal
component analysis (PCA), and inter-class and intra-class variations to
automatically determine the depth and width of a CNN model. The designed models
are lightweight, adapted to the internal structures of fundus images, and
encode the discriminative patterns of DR lesions. The technique is validated on
a local dataset from King Saud University Medical City, Saudi Arabia, and two
challenging benchmark datasets from Kaggle: EyePACS and APTOS2019. The
custom-designed models outperform the famous pre-trained CNN models like
ResNet152, Densnet121, and ResNeSt50 with a significant decrease in the number
of parameters and compete well with the state-of-the-art CNN-based DR screening
methods. The proposed approach is helpful for DR screening under diverse
clinical settings and referring the patients who may need further assessment
and treatment to expert ophthalmologists.
