Classification among Microaneurysms, Exudates, and Lesion free Retinal Regions in the Eye Images using Transfer Learned CNNs
Siddharth Gupta1, Avnish Panwar2, Silky Goel3

1Siddharth Gupta, CSE Department, Graphic Era Deemed to be University, Dehradun, India.
2Avnish Panwar, CSE Department, Graphic Era Deemed to be University, Dehradun, India.
3Silky Goel, CSE Department, UPES, Dehradun, India.
Manuscript received on November 21, 2019. | Revised Manuscript received on December 30, 2019. | Manuscript published on December 30, 2019. | PP: 5508-5512  | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4539129219/2019©BEIESP | DOI: 10.35940/ijeat.B4539.129219
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Abstract: When pancreas fails to secrete sufficient insulin in the human body, the glucose level in blood either becomes too high or too low. This fluctuation in glucose level affects different body organs such as kidney, brain, and eye. When the complications start appearing in the eyes due to Diabetic Mellitus (DM), it is called Diabetic Retinopathy (DR). DR can be categorized in several classes based on the severity, it can be Microaneurysms (ME), Haemorrhages (HE), Hard and Soft Exudates (EX and SE). DR is a slow start process that starts with very mild symptoms, becomes moderate with the time and results in complete vision loss, if not detected on time. Early-stage detection may greatly bolster in vision loss. However, it is impassable to detect the symptoms of DR with naked eyes. Ophthalmologist harbor to the several approaches and algorithm which makes use of different Machine Learning (ML) methods and classifiers to overcome this disease. The burgeoning insistence of Convolutional Neural Network (CNN) and their advancement in extracting features from different fundus images captivate several researchers to strive on it. Transfer Learning (TL) techniques help to use pre-trained CNN on a dataset that has finite training data, especially that in under developing countries. In this work, we propose several CNN architecture along with distinct classifiers which segregate the different lesions (ME and EX) in DR images with very eye-catching accuracies.
Keywords: Deep Learning, Hardexudates Logistic Regression, Random Forest, Machine Learning, Soft exudates.