Automated Detection of Macular Edema Using Machine Learning Algorithm

摘要

Macular Edema is an eye disease which is caused by the swelling of the macula and results in reduced central vision. Nowadays, so many techniques are there to detect macular diseases. And the latest non-invasive imaging technique is the OCT imaging technique, in which disorders can be detected at a very early stage. Many algorithms were implemented by the researchers for the detection of ME from OCT images. However, this paper proposes a computer-aided detection for the classification of ME from OCT images using Naive Bayes classifier. This classifier's main advantage is that classification from minimum features. Here the novelty is presented in the layer detection and texture feature extraction steps by developing a computer-aided detection algorithm for accurate ME identification. This paper provides a way to develop texture based machine learning algorithm for all biomedical imaging devices. Five distinct features (three based on the thickness profiles of the sub-retinal layers, one based on cyst fluid within the sub-retinal layers and one based on the texture feature) are extracted from the labeled images, and Naive Bayes is trained on these. The algorithm correctly classified 196 out of 200 OCT scan images (100 ME and 100 healthy). This algorithm achieves an accuracy, sensitivity, and specificity of 98%, 97%, 99%.