Diabetic retinopathy (DR) is the primary cause of preventable blindness in those within the working age group in developed nations. The macula, optic discs, and blood vessels that make up a healthy retina are irregularities that indicate a true eye condition. Therefore, retinopathy detection is crucial. Therefore, this study suggests using deep convolutional neural networks to build and predict different kinds of diabetic retinopathy. A pre-processing approach first initiates the input picture. The spatial processing method known as the Gaussian filter, which reduces picture noise and increases the clarity of fuzzy pictures, handles this procedure. The picture is sent to the regions for segmentation in the next stage. The size and form of the illness may be precisely determined using fuzzy C-means (FCM) segmentation. Additionally, it attempts to maintain as much difference between the clusters as well as much similarity between the intra-cluster data points. The next phase in the process is to use deep convolutional neural networks (DCNN). In order to extract features from the input picture, the convolutional layer applies filters. To expedite computation, the pooling layer samples the picture; the fully connected layer then generates the final prediction. The early detection of diabetic retinopathy has been made easier by the use of DCNN algorithms. Within the domain of medical image processing, the DCNN algorithm represents a methodically ordered approach and it shows 94.7%. This work offers a high degree of sensitivity and precision in identifying whether a picture has diabetic retinopathy or not. Creating the output image is the final stage. Python Google Colab software is used in the implementation of this paper.