alexa Abstract | Performance evaluation and comparative analysis of various machine learning techniques for diagnosis of breast cancer.

Biomedical Research
Open Access

OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)


Breast cancer is heterogeneous and life threatening diseases among women in world wide. The aim of this paper is to analyze and investigate a novel approach based on NSST (Shearlet transform) to diagnosis the digital mammogram images. Shearlet Transform is a multidimensional version of the composite dilation wavelet transform, and is especially designed to address anisotropic and directional information at various scales. Initially, using multi scale directional representation, mammogram images are decomposed into different resolution levels with various directions from 2 to 32. In this work we investigated five machine learning algorithm, namely SVM (Support Vector Machine), Naïve Bayes, KNN LDA and MLP, which are used to categorizes decomposed image as either cancerous (abnormal) or not (normal) and then again abnormal severity is further categorized as either benign images or malignant images. The evaluation of the system is carried out on the MIAS (Mammography Image Analysis Society) database. The tenfold cross-validation test is applied to validate the developed system. The performance of the five algorithms was compared to find the most suitable classifier. At the end of the study, obtained results shows that SVM is an efficient technique compares to other methods.

To read the full article Peer-reviewed Article PDF image | Peer-reviewed Full Article image

Author(s): Kanchanamani M Varalakshmi Perumal


Breast cancer, Benign, Malignant, Statistical features, Shearlet transform, Support vector machine, Naïve Bayes , KNN, LDA, MLP, #

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us