Tournament Based Ranking CNN for the Cataract grading
This work addresses the challenge of accurately grading medical conditions with imbalanced and ordinal data, offering a solution that could be applied to similar medical problems, though it appears incremental in its architectural improvements.
The paper tackles the problem of class imbalance and vague boundaries in medical datasets, specifically for cataract grading, by proposing a Tournament based Ranking CNN that achieves 68.36% exact match accuracy, outperforming baseline methods like Ranking CNN (53.40%) and pretrained ResNet (56.12%).
Solving the classification problem, unbalanced number of dataset among the classes often causes performance degradation. Especially when some classes dominate the other classes with its large number of datasets, trained model shows low performance in identifying the dominated classes. This is common case when it comes to medical dataset. Because the case with a serious degree is not quite usual, there are imbalance in number of dataset between severe case and normal cases of diseases. Also, there is difficulty in precisely identifying grade of medical data because of vagueness between them. To solve these problems, we propose new architecture of convolutional neural network named Tournament based Ranking CNN which shows remarkable performance gain in identifying dominated classes while trading off very small accuracy loss in dominating classes. Our Approach complemented problems that occur when method of Ranking CNN that aggregates outputs of multiple binary neural network models is applied to medical data. By having tournament structure in aggregating method and using very deep pretrained binary models, our proposed model recorded 68.36% of exact match accuracy, while Ranking CNN recorded 53.40%, pretrained Resnet recorded 56.12% and CNN with linear regression recorded 57.48%. As a result, our proposed method is applied efficiently to cataract grading which have ordinal labels with imbalanced number of data among classes, also can be applied further to medical problems which have similar features to cataract and similar dataset configuration.