Development of Efficient Image Quarrying Technique for Mammographic Image Classification to Detect Breast Cancer With Supervised Learning Algorithm

Conference proceedings article

Authors / Editors

Ravi, Samikannu

Research Areas

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Publication Details

Author list: Antony SJS, Ravi S

Publisher: IEEE

Place: NEW YORK

Publication year: 2013

Number of pages: 7

eISBN: 978-1-4799-3506-2

Languages: English-Great Britain (EN-GB)

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Abstract

This Breast cancer is one of the most prevalent lumps in women increased day by day around in worldwide. The scheme for the detection of breast cancer is Mammographic technique that is used at the very earlier stage. In this paper two kinds of classification Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) are used to analyze the mammographic images. The two classification methods are using the image pre-processing in wavelet decomposition and image enhancement. The results are verified with 322 mammogram images which is size for 1024x1024 with PGM format. The results show that the proposed algorithm can able to classify the images with a good performance rate of 98%. It can be concluded that supervised learning algorithm gives fast and accurate classification and it works as efficient tool for classification of breast cancer cells.

Keywords

2 dimensional discrete wavelet transform, Linear discriminant analysis, Mammographic technique, Support vector machines

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