title: Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier.
abstract: Prolonged diabetes retinopathy leads to diabetes maculopathy, which causes gradual and irreversible loss of vision. It is important for physicians to have a decision system that detects the early symptoms of the disease. This can be achieved by building a classification model using machine learning algorithms. Fuzzy logic classifiers group data elements with a degree of membership in multiple classes by defining membership functions for each attribute. Various methods have been proposed to determine the partitioning of membership functions in a fuzzy logic inference system. A clustering method partitions the membership functions by grouping data that have high similarity into clusters, while an equalized universe method partitions data into predefined equal clusters. The distribution of each attribute determines its partitioning as fine or coarse. A simple grid partitioning partitions each attribute equally and is therefore not effective in handling varying distribution amongst the attributes. A data-adaptive method uses a data frequency-driven approach to partition each attribute based on the distribution of data in that attribute. A data-adaptive neuro-fuzzy inference system creates corresponding rules for both finely distributed and coarsely distributed attributes. This method produced more useful rules and a more effective classification system. We obtained an overall accuracy of 98.55%.
authors.affiliation: Computer Science, Louisiana Tech University, Nethken Hall 121, 600 Dan Reneau Dr., #10348, Ruston, LA, 71272, USA.
authors.forename: Sulaimon
authors.initials: S
authors.lastname: Ibrahim
authors.affiliation: Computer Science, Louisiana Tech University, Nethken Hall 121, 600 Dan Reneau Dr., #10348, Ruston, LA, 71272, USA.
authors.forename: Pradeep
authors.initials: P
authors.lastname: Chowriappa
authors.affiliation: Computer Science, Louisiana Tech University, Nethken Hall 121, 600 Dan Reneau Dr., #10348, Ruston, LA, 71272, USA. sdua@latech.edu.
authors.forename: Sumeet
authors.initials: S
authors.lastname: Dua
authors.affiliation: Department of Electronics & Computer Engineering, Ngee Ann Polytechnic, 535 Clementi Road, Singapore, 599489, Singapore.
authors.forename: U Rajendra
authors.initials: UR
authors.lastname: Acharya
authors.affiliation: Department of Electronics & Communications, MIT Manipal, Manipal, 576104, India.
authors.forename: Kevin
authors.initials: K
authors.lastname: Noronha
authors.affiliation: Department of Ophthalmology, KMC Manipal, Manipal, 576104, India.
authors.forename: Sulatha
authors.initials: S
authors.lastname: Bhandary
authors.affiliation: Computer Science, Louisiana Tech University, Nethken Hall 121, 600 Dan Reneau Dr., #10348, Ruston, LA, 71272, USA.
authors.forename: Hatwib
authors.initials: H
authors.lastname: Mugasa
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003930:Diabetic Retinopathy / Q000145:classification* / Q000175:diagnosis*; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer
keywords: Classification; Diabetic retinopathy; Diagnosis; Fuzzy logic; Image analysis