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SREM-SqueezeNet hybrid architecture for edge detectionin Medical Images | ||
| Computational Sciences and Engineering | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 06 مرداد 1405 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22124/cse.2026.32285.1137 | ||
| نویسنده | ||
| Zohreh Dorrani* | ||
| Department of Electrical Engineering, Payame Noor University (PNU), Tehran, Iran. | ||
| چکیده | ||
| 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 | ||
| کلیدواژهها | ||
| Convolutional neural networks؛ Deep learning؛ Medical images؛ SqueezNet SREM–SqueezeNet | ||
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آمار تعداد مشاهده مقاله: 26 |
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