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Mitigating Data Imbalance Issues in Medical Image Analysis
Abstract
Medical images mostly suffer from data imbalance problems, which make the disease classification task very difficult. The imbalanced distribution of the data in medical datasets happens when a proportion of a specific type of disease in a dataset appears in a small section of the entire dataset. So analyzing medical datasets with imbalanced data is a significant challenge for the machine learning and deep learning community. A standard classification learning algorithm might be biased towards the majority class and ignore the importance of the minority class (class of interest), which generally leads to the wrong diagnosis of the patients. So, the data imbalance problem in the medical image dataset is of utmost importance for the early prediction of disease, specifically cancer. This chapter attempts to explore different problems concerning data imbalance in medical diagnosis. The authors have discussed different rebalancing strategies that offer guidelines for choosing appropriate optimal procedures to train the samples by a classifier for an efficient medical diagnosis.
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