A hybrid deep learning based assist system for detection and classification of breast cancer from mammogram images

K. Narayanan, R. Krishnan, Y. H. Robinson
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引用次数: 2

Abstract

The most common cancer disease among all women is breast cancer. This type of disease is caused due to genetic mutation of ageing and lack of awareness. The tumour that occurred may be a benign type which is a non-dangerous and malignant type that is dangerous. The Mammography technique utilizes the early detection of breast cancer. A Novel Deep Learning technique that combines the deep convolutional neural networks and the random forest classifier is proposed to detect and categorize Breast cancer. The feature extraction is carried over by the AlexNet model of the Deep Convolutional Neural Network and the classifier precision is increased by Random Forest Classifier. The images are collected from the various Mammogram images of predefined datasets. The performance results confirm that the projected scheme has enhanced performance compared with the state-of-art schemes.
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一种基于混合深度学习的辅助系统,用于从乳房x光图像中检测和分类乳腺癌
所有女性中最常见的癌症疾病是乳腺癌。这种疾病是由于衰老的基因突变和缺乏认识而引起的。发生的肿瘤可能是良性的,它是非危险的,恶性的是危险的。乳房x线照相术可以早期发现乳腺癌。提出了一种结合深度卷积神经网络和随机森林分类器的新型深度学习技术来检测和分类乳腺癌。特征提取由深度卷积神经网络的AlexNet模型进行,分类器精度由随机森林分类器提高。这些图像是从预定义数据集的各种乳房x光片图像中收集的。性能结果表明,与现有方案相比,该方案的性能有所提高。
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