Open Access Open Access  Restricted Access Subscription or Fee Access

A new optimal approach using NSVC for Breast Cancer Diagnosis Classification

M. Ngadi, A. Amine, H. Hachimi, A. El-Attar


Given the enormous number of mammograms performed during last years, computerbased diagnosis of breast cancer turned into a necessity. In particular, the diagnosis of breast masses and their classification currently arouse great interest. Indeed, the complexity of the processed forms and the difficulty encountered in order to discern them require the use of appropriate descriptors. This article is placed in the context of evaluating the results of supervised classification algorithms and their comparison. In this work, we conduct some experiments using the Wisconsin diagnosis Breast Cancer (WDBC) dataset in order to classify the dataset samples to be either benign or malignant. We show that the
best results are obtained using our new proposed neighboring Support Vector Classifier (NSVC).


SVM, NSVC, Classification,Breast Cancer Diagnosis.

Full Text:



  • There are currently no refbacks.

Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.