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- W4294067939 abstract "Breast cancer is a deadliest disease which is the second most leading cause of death in the world. There are 5 types of commonly occurring cancers which include Ductal carcinoma in situ (DCIS), Invasive ductal carcinoma (IDC), Tubular carcinoma, Medullary carcinoma, and Invasive lobular carcinoma (ILC). Early detection of breast cancer creates 93% or more chances of survival. So in order to detect breast cancer, one of the important morphological feature is mitotic count—showing the rate of division of cell. In a dataset, if one class have less number of examples compared to number of examples in the other class, then the dataset is imbalanced. Since there is much abundance of non-mitotic cells over mitotic cells, the dataset has class imbalance problem. This class skewness leads to inefficiency in classification task. At present there are many models which uses data sampling methods in order to decrease the class imbalance problem. But if there is a feature set that provides the exact decision boundary, then there is no need of sampling methods for solving the class imbalance issue. Therefore in this work a model is proposed which uses autoencoders to learn a feature set which has better classification capabilities of majority class and minority class to address the class imbalance problem. In this paper, Dual stacked autoencoder is applied on imbalanced mitotic and non-mitotic dataset, which showed improvement in f-measure and also reduces the overhead of smapling the dataset. The proposed dual stack autoencoder shown f-measure of 0.82, and outperformed many of the traditional classification methods." @default.
- W4294067939 created "2022-09-01" @default.
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- W4294067939 date "2022-09-02" @default.
- W4294067939 modified "2023-09-27" @default.
- W4294067939 title "A Novel Approach for Handling Imbalanced Data in Breast Cancer Dataset" @default.
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- W4294067939 doi "https://doi.org/10.1007/978-981-19-2840-6_54" @default.
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