Classification Methods to Improve Performance in Breast Cancer Screening

EasyChair Preprint 7477

Abstract

Breast cancer is a very aggressive type of cancer with a very low median survival. Today the deaths of women in the age group 15-55 are increasing because of malignant cells are increasing in breast. For the death of women it is the main cause. So, the possibility of improvement is only the early diagnosis of patients. Machine Learning (ML) techniques can assist the physicians by expanding tools for detection at initial stage and analysis of breast cancer thus increasing the probability of patient’s survival[1]. At present, mammography is the most effective imaging method used by radiologists for screening breast tumours. In this paper, author proposes a system using different classification method like Support Vector Machine (SVM), Naive Bayes, Decision tree and MLP (Multi Layer Perceptron) for early detection of cancer. Propose system extracts the texture based features and shape based features using LBP, GLCM, Otsu, Compactness, Fourier Transform. The main focus of the presented work is on application of MLP for breast cancer classification. In addition real time data has been used to improve accuracy. Proposed system will do the comparative study between both datasets by extracting the feature with and without removing pectoral muscles.

Keyphrases: ANN, Breast Cancer Detection, Convolutional Neural Network, Decision Tree, Deep Convolutional Neural Network, Fourier transform, MLP, Machine Learning Algorithm, Mammogram Image, Mammography, Medical Imaging, Naive Bayes, Real Time Dataset, SVM, automated breast ultrasound lesion, breast cancer, breast cancer screening, classification method, deep learning, medical image dataset, naive baye decision tree, neural network, pectoral muscle, pectoral muscles, preprocessing method, svm naive baye decision, time comparison graph

@booklet{EasyChair:7477,
  author    = {Snehal Mane and Vandana Jagtap},
  title     = {Classification Methods to Improve Performance in Breast Cancer Screening},
  howpublished = {EasyChair Preprint 7477},
  year      = {EasyChair, 2022}}