基于深度elm的乳腺癌可解释检测优化迁移学习

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS EAI Endorsed Transactions on Scalable Information Systems Pub Date : 2022-09-29 DOI:10.4108/eetsis.v9i6.1747
Ziquan Zhu, Shuihua Wang
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引用次数: 0

摘要

乳腺癌是女性最常见的恶性肿瘤之一,发病率呈逐年上升趋势。世界上每个国家的妇女都可能在青春期后的任何年龄患上乳腺癌。乳腺癌的病因尚不完全清楚。目前,乳腺癌检测的主要方法效率低下。研究人员正试图利用计算机技术检测乳腺癌。但仍有一些限制。方法:提出一种基于超声图像的乳腺癌检测网络(ODET)。在本文中,我们使用ResNet50作为骨干模型。我们通过基于深度elm的迁移学习对骨干模型进行了一些修改。经过这些修改后,网络被命名为DET。但是DET仍然存在一些不足,因为DET中的参数是随机分配的,在实验中不会改变。在本例中,我们选择BA对DET进行优化,优化后的DET命名为ODET。结果:所建立的ODET的F1评分(F1)、精密度(PRE)、特异度(SPE)、灵敏度(SEN)、准确度(ACC)分别为93.16%±1.12%、93.28%±1.36%、98.63%±0.31%、93.96%±1.85%、97.84%±0.37%。结论:ODET是一种有效的乳腺癌检测方法。
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ODET: Optimized Deep ELM-based Transfer Learning for Breast Cancer Explainable Detection
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.
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来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
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