Classification of Diabetic Retinopathy Features using Bag of Feature Model
Anil Kumar K R1, Noushira K I2, Meenakshy K3

1Anil Kumar K.R, Department of Electronics and Communication Engineering, NSS College of Engineering, Palakkad, Kerala, India. Research Scholar, University of Calicut, Kerala, India
2Noushira K.I, PG Scholar, Department of Electronics and Communication Engineering, NSS College of Engineering, Palakkad, Kerala, India.
3Meenakshy K, Department of Electrical and Electronics Engineering, Government Engineering College, Thrissur, Kerala, India..

Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 381-385 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4145119119/2019©BEIESP | DOI: 10.35940/ijitee.A4145.119119
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Abstract: Deep learning (DL) as well as feature learning by unsupervised methods have made tremendous consideration in the past decades because of its great and dynamic capacity to change input data into high level depictions by means of various machine learning (ML) methods and approaches. Therefore these interests have also showed a fast and steady growth in the arena of medical image analysis, especially in Diabetic Retinopathy (DR) classification. On contradiction, manual interpretation involves excessive processing time, large amount of expertise and work. Sternness of the DR is analyzed relative to the existence of Microaneurysms (MAs), Exudates (EXs) and Hemorrhages(HEs). Spotting of DR in its early stage is crucial and important to avoid blindness. This paper proposes an algorithm to build an automated system to extract the above mentioned DR features which are the elemental and initial signs of diabetic retinopathy. Initial step in this algorithm is preprocessing of the original image. The next step in this features extraction algorithms is elimination of optic disc (OD) and blood vessels which have similar characteristic with these features. Blood vessels are segmented using Multi-Level Adaptive Thresholding. OD is segmented using morphological operations. Feature extraction and classification is achieved by using deep Bag of Feature (BoF) model which uses Speeded Up Robust Features Our method achieved 100% acuuracy in DRIVE database and over 90% accuracy for e-OPTHA database. Thus, the proposed methodology represents a track towards precise and highly automated DR diagnosis on a large substantial scale along with better sensitivity and specificity.
Keywords:  Bag of Feature Model, Microaneurysms, Diabetic Retinopathy, Haemorrhages and Exudates.
Scope of the Article: Classification