Balancing Accuracy and Efficiency: CNN Fusion Models for Diabetic Retinopathy Screening

Diabetic retinopathy (DR) remains a leading cause of preventable blindness, yet large-scale screening is constrained by limited specialist availability and variable image quality across devices and populations. This work investigates whether feature-level fusion of complementary convolutional neural network (CNN) backbones can deliver accurate and efficient binary DR screening on globally sourced fundus images. Using 11,156 images pooled from five public datasets (APTOS, EyePACS, IDRiD, Messidor, and ODIR), we frame DR detection as a binary classification task and compare three pretrained models (ResNet50, EfficientNet-B0, and DenseNet121) against pairwise and tri-fusion variants. Across five independent runs, fusion consistently outperforms single backbones. The EfficientNet-B0 + DenseNet121 (Eff+Den) fusion model achieves the best overall mean performance (accuracy: 82.89\%) with balanced class-wise F1-scores for normal (83.60\%) and diabetic (82.60\%) cases. While the tri-fusion is competitive, it incurs a substantially higher computational cost. Inference profiling highlights a practical trade-off: EfficientNet-B0 is the fastest (approximately 1.16 ms/image at batch size 1000), whereas the Eff+Den fusion offers a favorable accuracy--latency balance. These findings indicate that lightweight feature fusion can enhance generalization across heterogeneous datasets, supporting scalable binary DR screening workflows where both accuracy and throughput are critical.

Parallel Computing Architectures for Robotic Applications: A Comprehensive Review

With the growing complexity and capability of contemporary robotic systems, the necessity of sophisticated computing solutions to efficiently handle tasks such as real-time processing, sensor integration, decision-making, and control algorithms is also increasing. Conventional serial computing frequently fails to meet these requirements, underscoring the necessity for high-performance computing alternatives. Parallel computing, the utilization of several processing elements simultaneously to solve computational problems, offers a possible answer. Various parallel computing designs, such as multi-core CPUs, GPUs, FPGAs, and distributed systems, provide substantial enhancements in processing capacity and efficiency. By utilizing these architectures, robotic systems can attain improved performance in functionalities such as real-time image processing, sensor fusion, and path planning. The transformative potential of parallel computing architectures in advancing robotic technology has been underscored, real-life case studies of these architectures in the robotics field have been discussed, and comparisons are presented. Challenges pertaining to these architectures have been explored, and possible solutions have been mentioned for further research and enhancement of the robotic applications.