Tri-Concomitant Local Feature Learning for Diabetic Retinopathy Classification
Santosh Nagnath Randive1, Ranjan Kumar Senapati2
1Santosh Nagnath Randive*, Research Scholar, Department of Electronics & Communication Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh, India.
2Ranjan Kumar Senapati, Professor, Department of Electronics & Communication Engineering, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram, Guntur, Andhra Pradesh India.
Manuscript received on July 12, 2019. | Revised Manuscript received on July 22, 2019. | Manuscript published on December 30, 2019. | PP: 143-147 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3115129219/2019©BEIESP | DOI: 10.35940/ijeat.B3115.129219
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Feature extraction, artificial neural network, Diabetic Retinopathy Classification.