Glaucoma Images Classification Using Fuzzy Min-Max Neural Network Based on Data-Core
S. Sri Abirami1, S.J Grace Shoba2
1Sri Abirami S, PG Student/Applied Electronics, Velammal Engineering College, Chennai, India.
2Mrs. S. J. Grace Shoba, Professor, Electronics and Communication Department, Velammal Engineering College, Chennai, India.

Manuscript received on June 05, 2013. | Revised Manuscript received on June 11, 2013. | Manuscript published on June 15, 2013. | PP: 9-15 | Volume-1 Issue-7, June 2013. | Retrieval Number: G0327061713/2013©BEIESP
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Abstract: Glaucoma is the major cause of blindness in worldwide. It is an ophthalmologist disease characterized by an increase in Intraocular Pressure (IOP). The types of glaucoma are primary open angle or chronic glaucoma (POAG) and closed angle (or) acute glaucoma (CAG) which causes a slow (or) rapid rise in Intraocular Pressure (IOP). The iridocorneal angle between the iris and the cornea is the key used to differentiate OAG and CAG. The stratus Anterior Optical Coherence Tomography (AS-OCT) images with these diseases are detected and classified from the normal images using the proposed fuzzy min-max neural network based on Data-Core (DCFMN). Data-core fuzzy min-max neural network (DCFMN) has strong robustness and high accuracy in classification. DCFMN contains two classes of neurons: classifying neurons (CNs) and overlapping neurons (OLNs).CNs are used to classify the patterns of data. The OLN can handle all kinds of overlap in different hyper boxes. A new type of membership function considering the characteristics of data and the influence of noise is designed for CNs in the DCFMN.
Keywords: Glaucoma, DCFMN, AS-OCT image.