Abstract:A large amount of data is the key requirement in order to train a neural network efficiently. Using a small size training set in network training causes low accuracy for model performance over the testing set and also hard to implement the model in practice. Similar to many other problems, sperm morphology datasets are also limited for training the neural network-based deep networks in order to provide an automatic evaluation of sperm morphometry. Data augmentation mitigates this problem by utilizing actual da… Show more
“…It can synthesize new spectra when the number of spectra is small; however, when the quality of the sample is not good, classification accuracy is poor. 23 Ma used the synthetic minority oversampling technique (SMOTE) to preprocess the collected imbalanced data to achieve the rapid identification of papillary thyroid carcinoma and micropapillary carcinoma. However, the uneven distribution of data was poor.…”
In this study, a data augmentation algorithm GKIM based on the Gaussian kernel density is proposed for the data augmentation of normal tissue spectra. It can effectively solve the class imbalance problem.
“…It can synthesize new spectra when the number of spectra is small; however, when the quality of the sample is not good, classification accuracy is poor. 23 Ma used the synthetic minority oversampling technique (SMOTE) to preprocess the collected imbalanced data to achieve the rapid identification of papillary thyroid carcinoma and micropapillary carcinoma. However, the uneven distribution of data was poor.…”
In this study, a data augmentation algorithm GKIM based on the Gaussian kernel density is proposed for the data augmentation of normal tissue spectra. It can effectively solve the class imbalance problem.
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