Predict Diabetic Retinopathy in Early-Stages: A Novel Ensemble Model using Efficient nets and an Automated System to Detect the Disease
Siddhartha Malladi1, S. Suguna Mallika2, Krishna Sai Prahlad M3, Sai Madhav Reddy Nomula4, Aadesh Pandiri5
1Siddhartha Malladi, Department of Information Technology, CVR College of Engineering, Mangalpally, Rangareddy District, Telangana, India.
2Dr. S. Suguna Mallika, Professor, Department of Computer Science and Engineering, CVR College of Engineering, Mangalpally, Rangareddy District (Telangana), India.
3Krishna Sai Prahlad M, Department of Computer Science and Engineering, CVR College of Engineering, Mangalpally, Rangareddy District, Telangana, India.
4Sai Madhav Reddy Nomula, University of Texas at Dallas, Campbell Rd, Richardson, TX 75080, USA.
5Aadesh Pandiri, Department of Computer Science and Engineering, CVR College of Engineering, Mangalpally, Rangareddy District, Telangana, India.
Manuscript received on 24 October 2022 | Revised Manuscript received on 31 October 2022 | Manuscript Accepted on 15 November 2022 | Manuscript published on 30 November 2022 | PP: 38-48 | Volume-11 Issue-12, November 2022 | Retrieval Number: 100.1/ijitee.L933511111222 | DOI: 10.35940/ijitee.L9335.11111222
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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 eye condition caused by high sugar levels inside the blood, which is the origin of excessive pressure inside blood vessels inside the eye, with the smallest vessels being the most vulnerable. This condition does not appear suddenly; rather, it develops gradually over time. After the disease progress, it can show symptoms like blurry vision, changes in vision from blurry to clear, and vice versa, blackspots or dark areas in the vision, poor night vision, fading out of colours, etc. Therefore, pre-emptive identification of disease is one of the beneficial tactics to prevent or get cured of this disease. This technique is also susceptible to human misjudgement, which exists in many clinical diagnoses. An Image Classification Model can accelerate the process of blindness detection in patients. We accomplish this by constructing a classifier using transfer learning that can extract key features from pictures and categorise them into separate stages. This work focused on making an efficient classifier with high accuracy and providing the patient with advance notice of their disease using an easy-to-use mobile application. Our model gave a 0.907 quadratic weighted kappa (QWK) score on independent test dataset and 93.2% accuracy on test time augmented data in multi-class classification. Furthermore, providing the necessary use cases with which the patient can track the diabetic retinopathy screening diagnosis
Keywords: Convolutional Neural Network, Deep Learning, Diabetic Retinopathy, Efficient Nets, Fundus Camera, Medical Image Analysis.
Scope of the Article: Deep Learning