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The Retina images suffer from the low gray level of contrast and less illumination in the region where it is nearby the optic disk with high brightness, while the region where it is far from the optic disk, has a lack of brightness thus can affect the extraction and can increment the computational time. This paper applies the enhancement of extraction and detection of retina images by reviewing the existing mechanisms and then performing experimental comparison of the developed solution through the integration of CLAHE (Contrast Limited Adaptive histogram equalization) and C-language techniques. The use of existing image enhancement mechanisms is with built-in function in Matlab, which is defined as adapthisteq (). The existing mechanism can enhance the contrast of the grayscale image by transforming the values using the Contrast limited adaptive histogram equalization. Based on the review, it was observed that there is still a need for a more timely and effective mechanism for enhancing the image quality in terms of its contrast and illumination. Hence, this study has an implemented image and enhanced mechanism with the use of CLAHE and C-language. In this integration, the c language codes involve the built-in function from the toolbox library of OpenCV (Open-Source Computer vision), like reading the Retina image and other functions. Then, the results produced between the existing mechanism and the new developed mechanism are compared. The difference between Matlab results and CLAHE integrated with C-language in the performed experiment shows the results for the verification of the Experimental is the developed solution …