Retinal images can be used to detect and follow up several important chronic diseases. The classification of retinal images requires an experienced ophthalmologist. This has been a bottleneck to implement routine screenings performed by general physicians. It has been proposed to create automated systems that can perform such task with little intervention from humans, with partial success. In this work, we report advances in such endeavor, by using a Lattice Neural Network with Dendritic Processing (LNNDP). We report results using several metrics, and compare against well known methods such as Support Vector Machines (SVM) and Multilayer Perceptrons (MLP). Our proposal shows better performance than other approaches reported in the literature. An additional advantage is that unlike those other tools, LNNDP requires no parameters, and it automatically constructs its structure to solve a particular problem. The proposed methodology requires four steps: (1) Pre-processing, (2) Feature computation, (3) Classification and (4) Post-processing. The Hotelling T(2) control chart was used to reduce the dimensionality of the feature vector, from 7 that were used before to 5 in this work. The experiments were run on images of DRIVE and STARE databases. The results show that on average, F1-Score is better in LNNDP, compared with SVM and MLP implementations. Same improvement is observed for MCC and the accuracy.
Keywords: Blood vessel segmentation; Dendritic processing; Diabetic retinopathy; Machine vision; Neural networks; Pattern recognition.
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