Visual perception relies on the retina, which converts incoming light into interpretable neural information. Diabetic retinopathy (DR), a complication arising from prolonged hyperglycemia, is a major contributor to progressive vision impairment and often remains undetected during its initial stages. The condition manifests in retinal imagery through distinct patterns, including high-intensity and low-intensity lesion regions such as exudates and hemorrhages. This paper proposes an automated framework for simultaneous identification of multiple lesion types in retinal fundus images. The approach begins with image refinement to improve visual quality, followed by intensity-driven segmentation to ex-tract candidate abnormal regions. Descriptive statistical measures—namely mean intensity, variance, standard deviation, and entropy—are computed to characterize these regions and are subsequently utilized as inputs to an Artificial Neural Network (ANN) for classification. To enhance reliability, the method incorporates mechanisms to exclude anatomically similar structures, particularly the optic disc and vascular components, thereby reducing false detections. Evaluation results confirm that the proposed system achieves effective separation between normal and pathological cases, indicating its potential utility in supporting early-stage screening of diabetic retinopathy.