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- W4313444767 abstract "Breast cancer is the most common cancer among Indian women. In every 4 min, one lady is diagnosed with breast cancers, and in every 13 min, one woman dies of breast cancer in India. Hence detection of the breast cancer at an early stage is very important. Fine needle aspiration cytology and histopathological biopsies are golden truth for detection of breast cancer. To increase the accuracy of detection, computer-aided diagnosis is used as a second opinion. In computer-assisted pathology analysis, breast cytological image classification is a most important task as it helps in understanding the structure and distribution of nucleus for the prediction of breast cancer. In this paper, comparative analysis is done using five different machine learning algorithms on publicly available Wisconsin diagnostic dataset from UCI machine learning repository. The accuracy of support vector machine, random forest, K-nearest neighbour, decision tree and logistic regression was found to be 97.36%, 95.61%, 96.49%, 94.73% and 97.36%, respectively. The authors conclude that support vector machine and logistic regression perform equally better in the terms of accuracy." @default.
- W4313444767 created "2023-01-06" @default.
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- W4313444767 date "2023-01-01" @default.
- W4313444767 modified "2023-09-26" @default.
- W4313444767 title "Comparison of Cell Nuclei Classification in Cytological Breast Images Using Machine Learning Algorithms" @default.
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- W4313444767 doi "https://doi.org/10.1007/978-981-19-2358-6_54" @default.
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