Diabetic retinopathy (DR) is one of the most prevalent genetic diseases in human and it is caused by damage to the blood vessels in the eye retina. If it is undetected and untreated at right time, it can lead to vision loss. There are many medical imaging and processing technologies to improve the diagnostic process of DR to overcome the lack of human experts. In the existing image processing methods, there are issues such as lack of noise removal, improper clustering segmentation and less classification accuracy. This can be accomplished by automatic diagnosis of DR using advanced image processing method. The cotton wool spot (CWS), hard exudates (HE) contains a common manifestation of many diseases in retina including DR and acquired immunodeficiency syndrome. In the present work, super iterative clustering algorithm (SICA) is proposed to identify the CWS, HE on retinal image. Feature-based medical image retrieval (FBMIR) datasets are utilized for this purpose. Noises present on the images and histogram-filtering technique is used to convert red, green, and blue (RGB) images into a perfect greyscale image without noise. After pre-processing, SICA is used to identify the CWS, HE detection on retinal images and eliminates unnecessary areas of interest. In the third stage, after detecting CWS and HE, various statistical features are extracted for further classification using deep assimilation learning algorithm (DALA). The performance of DALA technique is examined with various classification parameters like recall, precision, and F-measure. Finally, the false classification ratios are computed to compare the performance of the trained networks. The proposed method produces accurate detection of affected regions with an accuracy ratio of 98.5% and it is higher than the other conventional methods. This method may improve the accuracy of automatic detection and classification of eye diseases.