Efficient approaches for accuracy improvement of breast cancer classification using wisconsin database

Breast cancer is the second leading cause of death for women all over the world. But early detection and prevention can significantly reduce the chances of death. This paper deals with different statistical and deep learning analysis of Wisconsin Breast Cancer Database for improving the accuracy in...

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Bibliographic Details
Published in2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) pp. 792 - 797
Main Authors Shahnaz, Celia, Hossain, Jubaer, Fattah, Shaikh Anowarul, Ghosh, Shajib, Khan, Asir Intisar
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2017
Subjects
Algorithm design and analysis
algorithms
attributes
Breast cancer
Classification algorithms
Convolutional neural networks
deep learning
diagnosis
neural network
Support vector machines
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ISSN2572-7621
DOI10.1109/R10-HTC.2017.8289075

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Summary:Breast cancer is the second leading cause of death for women all over the world. But early detection and prevention can significantly reduce the chances of death. This paper deals with different statistical and deep learning analysis of Wisconsin Breast Cancer Database for improving the accuracy in detection and classification of breast cancer based on different attributes. Applying Naïve Bayes, SVM, Logistic Regression, KNN, Random Forest, MLP and CNN classifiers, higher accuracy is obtained which is up to 98% to 99%.
ISSN:2572-7621
DOI:10.1109/R10-HTC.2017.8289075