Fine Grained Classification of Mammographic Lesions using Pixel N-grams

  • Pradnya Kulkarni

Abstract

Breast cancer is the most common type of cancer
worldwide. Early diagnosis of breast cancer can result in
better treatment options increasing the survival chances of a
patient. Automated or computer aided detection of breast
cancer is applied in order to improve the accuracy and
turnover time. However, the accuracy of automated detection
systems can still be improved. Most of the efforts in the
computer aided detection systems classify the images into
cancerous and non-cancerous categories. The aim of this paper
is to classify the mammographic lesions into three categories
namely circumscribed, speculation and normal. The novel
Pixel N-gram features have been used for classification of
these lesions. Pixel N-grams are originated from character Ngram
concept of text categorization. Classification
performance is noted in order to analyse the effect of
increasing N and effect of using different classifiers (MLP,
SVM and KNN). It was observed that the classification
performance increases with increase in N and then starts
decreasing again. Moreover, classification performance
achieved using MLP classifier was better than the performance
using SVM or KNN classifiers.

Keywords: Classification, Mammograms, N-grams, SVM, MLP, KNN

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How to Cite
Kulkarni, P. (2019). Fine Grained Classification of Mammographic Lesions using Pixel N-grams. Asian Journal For Convergence In Technology (AJCT). Retrieved from http://asianssr.org/index.php/ajct/article/view/734
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