Artificial Intelligence

   

Automatic Retinal Disease Classification Using Machine Learning and AI

Authors: Tapesh Santra

patients get preventive care. Yet, due to lack of infrastructure and resources millions of patients do not avail such diagnosis. In this paper, I explore the possibility of developing an automatic retinal disease classifier using computer vision algorithms. Two different classes of algorithms are tested; (a) traditional computer vision approach of hand crafting features followed by developing machine learning (ML) models (b) automatic feature engineering and classification using more modern convolutional neural networks (CNN). The above algorithms were used to build both multi-class classifiers, i.e. the models which are trained to identify the correct disease, and binary classifiers, i.e. models that are trained to determine if a patient has a specific disease or not. A set of 600 pre-labelled retinal scan images were used to train the models. Both the ML and CNN models had relatively modest success in the multiclass scenario. However, the ML models were found to be reliably accurate in binary classification scenario, achieving >90% accuracy in identifying cataract.

Comments: 8 Pages. None

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Submission history

[v1] 2019-10-23 11:11:32

Unique-IP document downloads: 14 times

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