Elevating Healthcare AI: Achieving Efficiency and Accuracy in Medical Applications with Surrogate-Based Multiobjective Compression of ResNet50 CNNs

• Gabriel Bicalho Ferreira UFOP
• Pedro Silva UFOP
• Rodrigo Silva UFOP

Resumo

Machine learning, particularly convolutional neural networks (CNNs), has gained prominence in healthcare applications, including medical diagnosis and clinical support. However, the increasing size of CNNs poses challenges in resource-constrained medical devices and real-time applications. This paper explores the effectiveness of pruning and quantization on the ResNet50 model within the MedMNIST dataset, a valuable resource for medical image classification. The study evaluates a surrogate-based multiobjective compression method on three MedMNIST datasets: RetinaMNIST for diabetic retinopathy grading, DermaMNIST for disease categorization, and BloodMNIST for blood cell classification. Results demonstrate that the proposed compression method successfully identifies less computationally intensive models while maintaining or improving accuracy across all three healthcare-related datasets. A reduction of about 50% in inference time and an increase of more than 1% in accuracy were observed. These findings emphasize the practicality of compression techniques in healthcare applications, particularly for resource-constrained environments and real-time decision-making scenarios. This research opens avenues for further validation and exploration in other healthcare-related applications with higher-quality neural network models, ultimately enhancing the deployment of machine learning in the healthcare domain.