Investigations of severity level measurements for diabetic macular oedema using machine learning algorithms.

BACKGROUND: The macula is an important part of the human visual system and is responsible for clear and colour vision. Macular oedema happens when fluid and protein deposit on or below the macula of the eye and cause the macula to thicken and swell. Normally, it occurs due to diabetes called diabetic macular oedema. Diabetic macular oedema (DME) is one of the main causes of visual impairment in patients. AIM: The aims of the present study are to detect and localize abnormalities in blood vessels with respect to macula in order to prevent vision loss for the diabetic patients.
METHODS: In this work, a novel fully computerized algorithm is used for the recognition of various diseases in macula using both fundus images and optical coherence tomography (OCT) images. Abnormal blood vessels are segmented using thresholding algorithm. The classification is performed by three different classifiers, namely, the support vector machine (SVM), cascade neural network (CNN) and partial least square (PLS) classifiers, which are employed to identify whether the image is normal or abnormal.
CONCLUSION: The results of all of the classifiers are compared based on their accuracy. The classifier accuracies of the SVM, cascade neural network and partial least square are 98.33, 97.16 and 94.34%, respectively. While analysing DME using both images, OCT produced efficient output than fundus images. Information about the severity of the disease and the localization of the pathologies is very useful to the ophthalmologist for diagnosing disease and choosing the proper treatment for a patient to prevent vision loss.