An Aggregate Method for Thorax Diseases Classification

TL;DR

This paper introduces a novel weighted loss function and training optimization for deep neural networks to improve thorax disease classification from chest X-ray images, addressing class imbalance and multi-label challenges.

Contribution

It proposes a combined weighting scheme and training optimization method that enhances classification accuracy in thorax disease detection from X-ray images.

Findings

Improved classification performance over previous methods.

Effective handling of class imbalance in medical imaging.

Enhanced training efficiency with optimized deep network architecture.

Abstract

A common problem found in real-word medical image classification is the inherent imbalance of the positive and negative patterns in the dataset where positive patterns are usually rare. Moreover, in the classification of multiple classes with neural network, a training pattern is treated as a positive pattern in one output node and negative in all the remaining output nodes. In this paper, the weights of a training pattern in the loss function are designed based not only on the number of the training patterns in the class but also on the different nodes where one of them treats this training pattern as positive and the others treat it as negative. We propose a combined approach of weights calculation algorithm for deep network training and the training optimization from the state-of-the-art deep network architecture for thorax diseases classification problem. Experimental results on the Chest X-Ray image dataset demonstrate that this new weighting scheme improves classification performances, also the training optimization from the EfficientNet improves the performance furthermore. We compare the aggregate method with several performances from the previous study of thorax diseases classifications to provide the fair comparisons against the proposed method.