Liminal efficiency for AI general models: Investigating compression techniques for maximized performance in glaucoma diagnosis and prognosis

Abstract

Deep learning models have demonstrated remarkable performance across the glaucoma's diagnosis and prognosis; however, deploying them in resource-constrained environments poses significant challenges. This research explores the balance between compression and accuracy preservation in specialist convolutional neural networks (CNNs) intended for CPU-based execution with minimal storage requirements. By employing pruning, knowledge distillation, quantization, and weight sharing, it is aimed to achieve maximal compression without compromising essential task performance. Resulting findings provide insights into the efficiency limits of model compression and its implications for real-world deployment. Additionally, the applicability of these compression techniques to Transformer-based architectures is examined throughout the work, which pose unique challenges due to their reliance on attention mechanisms