ODET: Optimized Deep ELM-based Transfer Learning for Breast Cancer Explainable Detection

Abstract

INTRODUCTION: Breast cancer is one of the most common malignant tumors in women, and the incidence rate is increasing year by year. Women in every country in the world may develop breast cancer at any age after puberty. The cause of breast cancer is not fully understood. At present, the main methods of breast cancer detection are inefficient. Researchers are trying to use computer technology to detect breast cancer. But there are some still limitations. METHODS: We propose a network (ODET) to detect breast cancer based on ultrasound images. In this paper, we use ResNet50 as the backbone model. We make some modifications to the backbone model by deep ELM-based transfer learning. After these modifications, the network is named DET. However, DET still has some shortcomings because the parameters in DET are randomly assigned and will not change in the experiment. In this case, we select BA to optimize DET. The optimized DET is named ODET. RESULTS: The proposed ODET gets the F1-score (F1), precision (PRE), specificity (SPE), sensitivity (SEN), and accuracy (ACC) are 93.16%±1.12%, 93.28%±1.36%, 98.63%±0.31%, 93.96%±1.85%, and 97.84%±0.37%, respectively. CONCLUSION: It proves that the proposed ODET is an effective method for breast cancer detection.

E-GVD: Efficient Software Vulnerability Detection Techniques Based on Graph Neural Network

“…However, these algorithms can mostly only predict whether the source code at the block or function level is vulnerable, and cannot provide more comprehensive location information related to the vulnerability. In view of this issue, we attempt to integrate interpretable techniques, which have been widely applied in various domains [16,17], on top of function-level vulnerability detection methods, thereby providing information related to vulnerability code lines while ensuring the performance of vulnerability detection.…”