Biomedia ACM MM Grand Challenge 2019: Using Data Enhancement to Solve Sample Unbalance

The Biomedia ACM MM Grand Challenge focuses on medical applications with a task to detect and classify abnormalities within gastrointestinal (GI) tract. As a part of the submission for this challenge, several methods we applied are reported in this paper. The data we used is from the KVASIR dataset and the NEETHUS dataset. It contains training and test data in form of image or video. The main challenge of this task is the data's insufficiency and unbalance, which will significantly decrease the performance. To solve this problem and achieve better result, we conduct multiple data enhancement operations on the data. The method is proved to be efficient. Apart from the operations applied on the data, we also test several classification structures include SCNN (Shallow Convolutional Neural Network), SCNN-SVM (Support Vector Machine), ResNet32-SVM, SVM, ResNet16, ResNet32, ResNet50, ResNet101 and Residual Attention Network. We finally chose ResNet50 as the main structure considered with the balance of accuracy and efficiency. We obtain 91.51% precision, 87.45% sensitivity, 99.48% specificity, 87.93% F1-score (the harmonic mean of precision and sensitivity) and 91.40% MCC (Matthews correlation coefficient) on the test dataset.