Abdelsamea Mohammed M, Mohamed Marghny H, Bamatraf Mohamed
Department of Mathematics, Assiut University, Assiut, Egypt.
School of Computer Science, Nottingham University, Nottingham, UK.
Cancer Inform. 2019 Jun 16;18:1176935119857570. doi: 10.1177/1176935119857570. eCollection 2019.
We propose a novel neural network approach for the classification of abnormal mammographic images into benign or malignant based on their texture representations. The proposed framework has the capability of mapping high dimensional feature space into a lower-dimension, in a supervised way. The main contribution of the proposed classifier is to introduce a new neuron structure for map representation and adopt a supervised learning technique for feature classification. This is achieved by making the weight updating procedure dependent on the class reliability of the neuron. We showed high accuracy (95.2%) for our proposed approach in the classification of abnormal real mammographic images when compared to other related methods.
我们提出了一种新颖的神经网络方法，用于根据乳腺钼靶异常图像的纹理特征将其分类为良性或恶性。所提出的框架能够以监督的方式将高维特征空间映射到低维空间。所提出的分类器的主要贡献在于引入了一种用于映射表示的新神经元结构，并采用监督学习技术进行特征分类。这是通过使权重更新过程依赖于神经元的类别可靠性来实现的。与其他相关方法相比，我们所提出的方法在对真实乳腺钼靶异常图像进行分类时显示出了较高的准确率（95.2%）。
