A Review on the Rule-Based Filtering Structure with Applications on Computational Biomedical Images

Abstract

In this paper, we present rule-based fuzzy inference systems that consist of a series of mathematical representations based on fuzzy concepts in the filtering structure. It is crucial for understanding and discussing different principles associated with fuzzy filter design procedures. A number of typical fuzzy multichannel filtering approaches are provided in order to clarify the different fuzzy filter designs and compare different algorithms. In particular, in most practical applications (i.e., biomedical image analysis), the emphasis is placed primarily on fuzzy filtering algorithms, with the main advantages of restoration of corrupted medical images and the interpretation capability, along with the capability of edge preservation and relevant image information for accurate diagnosis of diseases.

Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control

“…In recent advancements, it has been noticed that most works are primarily focused on CNN-based models, whilst alternative deep learning strategies such as the fuzzy-model-based nonlinear classification scheme are limited, despite the fact that this technique outperforms a single model. Fuzzy-model-based nonlinear classification systems with reduced fault classification and reduced fault-tolerant control problems for various image processing, especially as applied to medical image processing tasks would be an interesting topic [118][119][120][121].…”