Fundamental study on class classification using deep metric learning

Abstract

Deep Learning has expanded the use of AI, particularly in healthcare, where it is used for primary screening to exclude normal samples. However, conventional classification models lack confidence in their judgments, leading to potential misclassifications. Therefore, some of the authors enabled mapping of image ambiguity to specific positions by introducing new parameters into the loss function of Siamese Networks. And they proposed a classification model inspired by radar charts. However, the effectiveness of this approach has not been extensively discussed so far. So, we aim to improve this model by determining endpoints based on class data distribution, ensuring accurate and error-free classification.