Main Article Content
Ocular disease, spatial correlation network, CNN, eye disease
Ocular diseases, ranging from diabetic retinopathy to glaucoma, pose a substantial global healthcare challenge, demanding accurate and timely diagnosis. The conventional manual interpretation of ocular imaging, while practiced with expertise, is subjective and labor-intensive. This research addresses the pressing need for automation in ocular disease diagnosis and classification through the innovative application of Spatial Correlation Networks (SCNs).
Our objectives encompass the development of a dedicated SCN framework, comprehensive dataset collection, standardized annotation, and the automated detection and classification of ocular diseases. Performance evaluation considers accuracy, sensitivity, specificity, and computational efficiency, alongside the potential for clinical integration. Ethical and regulatory dimensions are thoughtfully explored. The author has developed a patient-level multi-label OD (PLML_ODs) classification model based on a spatial correlation network (SCNet). This model takes into account the patient-level diagnosis by combining data from both eyes and performing multi-label ODs classification. The PLML_ODs model comprises three key components: a backbone convolutional neural network (CNN) used for feature extraction, specifically DenseNet-169; the SCNet for capturing feature correlations; and a classifier for generating classification scores.
DenseNet-169 is responsible for extracting two distinct sets of attributes, one from each of the left and right CFI. Subsequently, the SCNet records correlations between these two sets of features at a pixel-by-pixel level. After the attributes have been analyzed, they are integrated to create a patient-level representation. This patient-level representation is utilized throughout the entire process of ODs categorization.
To evaluate the effectiveness of the PLML_ODs model, a soft margin loss function is applied to a publicly accessible dataset. The results demonstrate a significant improvement in classification performance when compared to several baseline approaches.
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