  This paper presents dilated Residual Network (ResNet) models for disease
classification from retinal fundus images. Dilated convolution filters are used
to replace normal convolution filters in the higher layers of the ResNet model
(dilated ResNet) in order to improve the receptive field compared to the normal
ResNet model for disease classification. This study introduces
computer-assisted diagnostic tools that employ deep learning, enhanced with
explainable AI techniques. These techniques aim to make the tool's
decision-making process transparent, thereby enabling medical professionals to
understand and trust the AI's diagnostic decision. They are particularly
relevant in today's healthcare landscape, where there is a growing demand for
transparency in AI applications to ensure their reliability and ethical use.
The dilated ResNet is used as a replacement for the normal ResNet to enhance
the classification accuracy of retinal eye diseases and reduce the required
computing time. The dataset used in this work is the Ocular Disease Intelligent
Recognition (ODIR) dataset which is a structured ophthalmic database with eight
classes covering most of the common retinal eye diseases. The evaluation
metrics used in this work include precision, recall, accuracy, and F1 score. In
this work, a comparative study has been made between normal ResNet models and
dilated ResNet models on five variants namely ResNet-18, ResNet-34, ResNet-50,
ResNet-101, and ResNet-152. The dilated ResNet model shows promising results as
compared to normal ResNet with an average F1 score of 0.71, 0.70, 0.69, 0.67,
and 0.70 respectively for the above respective variants in ODIR multiclass
disease classification.
