Automatic Cancer Detection Using Segmentation, Supervised and Unsupervised Techniques

Abstract This paper proposed a new diagnosis technique for Brain cancer detection. The features are extracted with supervised and unsupervised classification techniques. Also segmentation technique is used for diagnosis of the brain as normal or abnormal. The MRI images are given as a input to DWT for filtering. Then filtered image is given to PCA(Principle Component Analysis) to reduce the size of the matrix obtained from DWT. The supervised technique FP-ANN(Feed forward back propagation artificial neural network) and unsupervised technique K-NN(K-Nearest neighbor) are used to predict whether the tumor is normal or abnormal. Also the segmentation of the input brain images are done using K-means clustering to identify the mass of tissues or tumor. The features such as skewness, kurtosis, mean, variance, Standard deviation, Energy, Entropy are extracted. Also the Sensitivity, Specificity, Accuracy, PPV(Positive Predictive value),NPV(Negative Predictive value).FDR(False discovery rate) and MCC(Mathews correlation coefficient) are calculated. The accuracy of the proposed system is found to be 98.5% with k-NN and 95.71with FP_ANN.