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.

This paper focuses on real-time anomaly detection in surveillance video using YOLOv8, the latest in the YOLO object detection series, integrated with spatio-temporal analysis. The system aims to detect abnormal behavior in crowded environments by combining spatial object detection with temporal activity analysis. YOLOv8 is used to detect and track individuals in video frames, while a 3D Convolutional Neural Network (3D CNN) processes sequences of frames to identify behavioral anomalies based on movement patterns. Three variants of YOLOv8—Nano (n), Small (s), and Medium (m)—are evaluated for performance trade-offs in accuracy, processing speed (FPS), and latency. Results show YOLOv8n offers the best real-time performance, while YOLOv8m provides higher accuracy at the cost of increased latency. The system uses the UCF-Crime dataset for training and testing, and metrics such as accuracy, FPS, and latency are used for evaluation. The modular pipeline supports scalability and real-time deployment, with visual outputs aiding interpretation. By integrating object detection with spatio-temporal modelling, the system effectively identifies anomalies such as loitering or sudden movements. Future work includes refining detection accuracy using labelled anomalies and exploring advanced models like Transformers for improved temporal understanding. The significance of this research lies in its ability to combine lightweight real-time object detection with effective temporal behavior modeling within a scalable and modular architecture. The proposed framework contributes to the advancement of intelligent surveillance systems by improving anomaly detection reliability while maintaining computational efficiency suitable for deployment in smart cities, public safety monitoring, and edge-based surveillance applications.