In today's world, eye defects are more common among people of all ages. Most serious disorders of the eye include retinal detachment, macular degeneration. Technology is ever emergent and improving the way that assists in analyzing fundal images. Smart phones deployed with android applications leads to promising means for significant enrichment in eye care aids. In this paper an, 'add-on' for effective detection of such eye defects is presented that can be incorporated in smartphones. The developed add-on initially acquires L*a*b*triplets of the given fundus image. The resulting L*a*b*triplets is then contrast enhanced for further fundus examination as the image is captured under non-uniform illumination environment. Subsequent steps involve feature extraction and defect classification with Artificial neural network based Back Propagation Algorithm. Performance analysis of the proposed system is evaluated using fundus images attained from DRIVE, MESSIDOR and STARE database. The ROC analysis depicts a consistent performance and 90% classification accuracy for images of different database. Hence, this application improves efficacy of retinal diagnosis and aids in timely assessment of retinal disorders. This application is developed in android platform and is compatible with existing smartphones, augmenting its features. A step by step procedure for installation and operation of the add-on in smartphones is also presented in the paper. Copyright 2014, ISA All Rights Reserved.