Automatic detection of maculopathy disease is a very important step to achieve high‐accuracy results for the early discovery of the disease to help ophthalmologists to treat patients. Manual detection of diabetic maculopathy needs much effort and time from ophthalmologists. Detection of exudates from retinal images is applied for the maculopathy disease diagnosis. The first proposed framework in this paper for retinal image classification begins with fuzzy preprocessing in order to improve the original image to enhance the contrast between the objects and the background. After that, image segmentation is performed through binarization of the image to extract both blood vessels and the optic disc and then remove them from the original image. A gradient process is performed on the retinal image after this removal process for discrimination between normal and abnormal cases. Histogram of the gradients is estimated, and consequently the cumulative histogram of gradients is obtained and compared with a threshold cumulative histogram at certain bins. To determine the threshold cumulative histogram, cumulative histograms of images with exudates and images without exudates are obtained and averaged for each type, and the threshold cumulative histogram is set as the average of both cumulative histograms. Certain histogram bins are selected and thresholded according to the estimated threshold cumulative histogram, and the results are used for retinal image classification. In the second framework in this paper, a Convolutional Neural Network (CNN) is utilized to classify normal and abnormal cases. Automatic detection of maculopathy disease is a very important step to achieve high‐accuracy results for the early discovery of the disease and help ophthalmologists to treat patients. Manual detection of diabetic maculopathy needs much effort and time from ophthalmologists. Detection of exudates in eye images is applied for the maculopathy disease diagnosis. The proposed framework for retinal image classification begins with fuzzy processing for image improvement. The aim of this step is to enhance the contrast between objects and the background. After that, image segmentation is performed after binarization of the image to extract both blood vessels and the optic disc and then remove them. A gradient process is performed on the retinal images for discrimination between normal and abnormal cases. Histogram of the gradients is estimated and consequently, the cumulative histogram is obtained. Cumulative histogram of images with exudates and images without exudates are obtained. Histogram bins are thresholded to give a threshold cumulative histogram that can be used for retinal images classification. A Convolutional Neural Network (CNN) is performedutilized to classify normal and abnormal cases.