An approach for Breast Cancer classification using Neural Networks

D. Gladis, S. Vijaya
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Abstract

- Breast Cancer,an increasing predominant death causing disease among women has become a social concern. Early detection and efficient treatment helps to reduce the breastcancerrisk.AdaptiveResonanceTheory(ART1),anunsupervised neural network has become an efficient tool in the classification of breast cancer as Benign(non dangerous tumour) or Malignant (dangerous tumour). 400 instances were pre processed to convert real data into binary data and the classification was carried out using ART1 network. The results of the classified data and the physician diagnosed data were compared and the standard performance measures accuracy, sensitivity and specificity were computed. The results show that the simulation results are analogous to the clinical results.
基于神经网络的乳腺癌分类方法
乳腺癌是妇女中日益严重的主要致死疾病,已成为社会关注的问题。早期发现和有效治疗有助于降低乳腺癌的风险。自适应共振理论(ART1),无监督神经网络已成为乳腺癌分类为良性(非危险肿瘤)或恶性(危险肿瘤)的有效工具。对400个实例进行预处理,将真实数据转换为二进制数据,并利用ART1网络进行分类。比较分类数据和医师诊断数据的结果,计算标准性能指标的准确性、敏感性和特异性。结果表明,模拟结果与临床结果接近。
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