Open AccessTri Concomitant Local Feature Learning for Diabetic Retinopathy Classification
Author(s) -
Santosh Nagnath Randive,
Ranjan K. Senapati
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.b3115.129219
Subject(s) - pattern recognition (psychology) , artificial intelligence , computer science , feature extraction , artificial neural network , feature (linguistics) , correlation , diabetic retinopathy , pixel , concomitant , stage (stratigraphy) , mathematics , medicine , statistics , paleontology , philosophy , linguistics , biology , diabetes mellitus , endocrinology , geometry
In this paper, we have proposed a new technique entitled as Transformed Directional Tri Concomitant Triplet Patterns with Artificial Neural Network is proposed for Diabetic Retinopathy Classification. TdtCTp consist of three stages to obtain detail directional information about pixel progression. In first stage, structural rule based approach is proposed to extract directional information in various direction. Further, in second stage, microscopic information and correlation between each sub-structural element are extracted by using concomitant conditions. Finally, minute directional intensity variation information and correlation between the sub-structural elements are extracted by integrating first two stages. After feature extraction, the extracted feature is used as input to the artificial neural network. To the best of our knowledge, this is the first learning based approach for diabetic retinopathy classification. Effectiveness of the proposed method is evaluated in terms of average precision and compared with existing state-of-the-art methods. The experimental analysis shows that the proposed method is achieved significant performance compared to other methods.