Abstract:
: Choroidal Neovascularization (CNV) is a devastating sequela resulting from a wide range of disorders that
affect the Retinal Pigment Epithelium (RPE)-Bruch's membrane-choriocapillaris complex. That is popularly known
as Age Regulated Degeneration (AMD), which is the leading cause of permanent serious loss of vision among senior
citizens in the developed parts of the world. A mathematical model implemented in this work is used to detect the
stages of CNV using artificial intelligence technique. This model is developed based on the intensity variation caused
by the size of lesions in the FA (fluorescein angiography) sequence with respect to time. A collection of features is
calculated from the mathematical model, and a feature vector is constructed using those features. These feature
vectors are used to categorize the severity of the disease through a classifier. The mathematical model of lesions in
CNV is used to find out the pattern that CNV follows which enhances the pattern recognition capabilities of the
classifier. Defining CNV lesions is typically a time-consuming and tiresome task. Therefore, this proposed method of
developing a mathematical model of the intensity variation in the FAs will help to identify CNV efficiently.
Irrespective of the machine learning classifiers, this model provides good accuracy, sensitivity, and specificity and F1
score
