In recent years, breast cancer is recognized as critical disease that needs to be detected at early stage. Breast cancer can be detected either by mammography or biopsy technique. Biopsy is cost effective procedure for breast cancer detection. Fine needle aspiration (FNA) digital image is pre-processed and extracted features from it used for classification. Based on the parameters of feature attributes, they are classified into two classes: malignant (cancerous) and benign (not cancerous) tumor cell. Manual analysis and classification of this image is very difficult and challenging task. The need of automation for detecting and classifying these tumor cells in cost effective and accurate manner provokes many researchers in this field. With this aim we proposed PCA–LDA based feature extraction and reduction (FER) technique that reduce the original feature space to large extent and performing training over this reduced set that give excellent accuracy of 98.6%. For classification we use ANNFIS classifier that uses the neural-network concept with some fuzzy rule logic. We perform comparative performance analysis study amongst our proposed work over two other classifiers i.e. support vector machine (SVM) and multi-layer perceptron (MLP). The experimental result shows that proposed framework outperform over SVM and MLP with an accuracy of 98.6%.

近年来，乳腺癌被认为是需要在早期发现的严重疾病。乳腺癌可以通过乳房X线照相术或活检技术来检测。活检是检测乳腺癌的经济有效的方法。细针抽吸（FNA）数字图像经过预处理，并从中提取特征用于分类。根据特征属性的参数，将它们分为两类：恶性（癌性）和良性（非癌性）肿瘤细胞。手动分析和分类此图像是非常困难且具有挑战性的任务。以成本有效且准确的方式来自动检测和分类这些肿瘤细胞的需求激起了该领域的许多研究人员。为此，我们提出了基于PCA–LDA的特征提取和归约（FER）技术，该技术可在很大程度上减少原始特征空间，并在此缩减集上进行训练，从而获得98.6％的出色准确性。对于分类，我们使用ANNFIS分类器，该分类器使用带有模糊规则逻辑的神经网络概念。我们在其他两个分类器（即支持向量机（SVM）和多层感知器（MLP））的拟议工作中进行比较性能分析研究。实验结果表明，提出的框架优于SVM和MLP，准确度达到98.6％。我们在其他两个分类器（即支持向量机（SVM）和多层感知器（MLP））的拟议工作中进行比较性能分析研究。实验结果表明，提出的框架优于SVM和MLP，准确度达到98.6％。我们在其他两个分类器（即支持向量机（SVM）和多层感知器（MLP））的拟议工作中进行比较性能分析研究。实验结果表明，提出的框架优于SVM和MLP，准确度达到98.6％。