Document Type : Original Article

Author

• Zohreh Dorrani

Department of Electrical Engineering, Payame Noor University (PNU), Tehran, Iran.

Abstract

This study introduces a hybrid deep learning approach for medical image edge detection, integrating the SREM–SqueezeNet architecture with mathematical optimization techniques to enhance accuracy and computational efficiency. The proposed framework employs the lightweight and parameter-efficient structure of SqueezeNet, which enables high-performance edge extraction while maintaining a compact model suitable for deployment on resource-constrained medical systems. The research emphasizes a mathematical formulation of the convolutional neural networks optimization process, incorporating evaluation metrics such as entropy, precision, recall, F-measure, true positive rate, and accuracy to quantitatively assess edge detection quality. Experimental results demonstrate the superiority of the proposed method, achieving a significant reduction in entropy to 0.1153 and an improvement in the F-measure to 0.9154, outperforming conventional edge detection techniques. These outcomes highlight the potential of the mathematically optimized SREM–SqueezeNet hybrid model as an effective and reliable solution for medical image analysis, contributing to improved diagnostic precision and automated disease detection in clinical applications

Keywords

• Convolutional neural networks
• Deep learning
• Medical images
• SqueezNet SREM–SqueezeNet