An expert system to distinguish a defective eye from a normal eye

Eyes are the organs of vision. Detection of most common differentiating characteristics of eye diseases from the fundus images of the retina can be a good approach as an automatic and low-cost method for broad-classification initial screening. For example in early diabetic retinopathy detection enables application of modern treatment in order to prevent or delay the loss of vision. The paper has referenced Diabetic retinopathy and Retinitis pigmentosa for analysis purpose. Automated approach for detection of microaneurysms in digital color retinal fundus photographs helps ophthalmologist to detect the emergence of its initial symptoms and determine the next immediate action step for the patient. A similar mechanism for automated early disease detection method is proposed featuring identification of dark pigments like minute features, exudate and microaneurysm detection and these features extracted can prove to a greater extent as primary instances for defectiveness of eye. A good number of images along with the response from the ophthalmologist has proved to be a great help towards the observation as derived from this mechanism and discussed in the paper. The proposed mechanism can be extended up to the limit of supervised learning so as to automate the practical responses as obtained from the ophthalmologist in real time scenario.