Diabetic Retinopathy Screening using Machine Learning for Hierarchical Classification
Nandana Prabhu1, Deepak Bhoir2, Nita Shanbhag3
1Nandana Prabhu, Department of Electronics Engineering, Fr. Conceicao Rodrigues College of Engineering, Mumbai, India.
2Deepak Bhoir, Department of Electronics Engineering, Fr. Conceicao Rodrigues College of Engineering, Mumbai, India.
3Nita Shanbhag, Department of Ophthalmology, D Y Patil University & School of Medicine, Navi Mumbai, India.
Manuscript received on 02 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 1943-1948 | Volume-8 Issue-10, August 2019 | Retrieval Number: J92770881019/2019©BEIESP | DOI: 10.35940/ijitee.J9277.0881019
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© 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: Diabetic Retinopathy is a consequence of prolonged unaddressed diabetes. It is currently diagnosed by the subjective clinical examination and manual grading of the fundus images by the ophthalmologists. This disease is progressive in nature. Hence early detection and treatment go a long way in helping the patients fight the dire consequences of the disease. Given the fact that number of ophthalmologists is very less as compared to the patients, a cost-effective, computer assisted, automated retina analysis system is highly desirable for the rural health care. This paper proposes an automatic Diabetic Retinopathy detection system based on the presence of bright lesions on the retina which is one of the symptoms of Diabetic Retinopathy. Initially the optic disc is removed from the fundus image as its brightness is similar to that of the bright lesions. Exudates are extracted and its various features are obtained. Later, feature based hierarchical classification is performed for detection of different stages of the disease. This method is based on the same logical steps as followed by the ophthalmologists and hence assures more accurate classification results. Two methodologies, Random Forest algorithm and Artificial Neural Network are explored and accuracy, sensitivity and specificity are evaluated at each stage of classification. The former outperformed the latter. The accuracy obtained using Random Forest are 100%, 85.71% and 87.5% and Artificial Neural network are 100%,78.5% and 66.67% for Stage 1, Stage 2 and Stage 3 respectively.
Keywords: Artificial Neural Network, Exudates, Non Proliferative, Random Forest, Retinopathy.
Scope of the Article: Machine Learning