Classification of Breast Cancer using PNN Classifier
Anushree N.R, Roshni A Ramesan , Sirasappa.Y. Pattar
PNN classifier, GMM segmentation, breast cancer, mammography, Image processing, DWT, Noise reduction.
To detect any disease and to monitor the patients having these diseases, involvement image processing technique has a major role to play. One of the most important elements is breast cancer detection. It is a difficult task to segment the tumour cells in breast because of the low contrast issues and the images won’t be that clear. A good technique has been developed here where the noise is removed and some improvements will be done on the images so that diagnosis can be done perfectly. After that the image will be segmented, here GMM segmentation method is used along with thresholding method for segmentation of the boundaries of the breast so that the tumour region can be determined of the picture. The next step is feature extraction which is done using Discrete Wavelet Transform (DWT). Probabilistic Neural Network (PNN) with radial basis function is used to classify Breast Tumour whether it is benign or malignant. If the breast tumour is detected in early stage, then it could save many lives. Here for automated breast tumour classification for excellent classification, feature extraction and segmentation should be perfect.
Article Details
Unique Paper ID: 152968

Publication Volume & Issue: Volume 8, Issue 5

Page(s): 125 - 130
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