Constructing Fuzzy Linguistic Variables Using Crisp Decision Trees. An Application to Diabetic Retinopathy

Identifier:  imarina:9469686

Handle: https://hdl.handle.net/20.500.11797/imarina9469686

Authors:  Esmail A; Pascual-Fontanilles J; Garcia E; Valls A; Baget M; Romero-Aroca P

Abstract:
Diabetic retinopathy is a leading cause of vision loss worldwide. Early detection and understanding of its progression are crucial for preventing serious complications. This work is based on assessing the severity of diabetic retinopathy based on features extracted from eye fundus images. We use LezioSeg, a deep learning-based computer vision model, to segment and locate retinopathy lesions. Specifically, we quantify the number of microaneurysms and hemorrhages in the four retinal quadrants. These features are then used to train Retiprogram, a multiclass classification model based on a Fuzzy Random Forest. The goal is to identify the actual level of Diabetic Retinopathy. A key challenge with Fuzzy Random Forests is the definition of appropriate linguistic variables. This paper proposes a novel method for automatically generating these fuzzy sets from decision values identified at constructing crisp decision trees from a labeled set of examples. These automatically generated fuzzy sets serve as linguistic variables for the Retiprogram. We use crisp Random Forests to evaluate the Retiprogram's performance against both Yuan and Shaw's fuzzification method. The proposed method outperforms the standard both in accuracy and kappa indicators.