Assessment of Data Augmentation Paradigms in Pathology Identification

Data augmentation has an important role in improving the performance of models in medical realm. This research shows the impact of various data augmentation techniques by evaluating its performance using two medical datasets. By employing augmentation strategies such as under sampling and oversampling techniques to balance imbalanced datasets which includes Repeated Edited Nearest Neighbors (RENN), Random under sampling (RUS), Near Miss and SVM, Borderline, Simple Smote techniques. This study explores the connection between data preprocessing, model development and the effectiveness of various data augmentation techniques. Three different deep learning models-Artificial Neural Networks (ANN), Long Short-Term Memory networks (LSTM), and its varient Gated Recurrent Units (LSTM) were used on each method, and their performance was observed and analyzed. The comparison of these models on different augmentation techniques provides crucial insights into how each method affects the models performance. These insights can be useful in the real time where the data is not distributed equally such as in medical, security industry. In this research two medical datasets related to heart and cancer were taken and experimented with sampling methods which have imbalance labels.