Effective-LDAM: An Effective Loss Function To Mitigate Data Imbalance for Robust Chest X-Ray Disease Classification

TL;DR

This paper introduces E-LDAM, a novel loss function designed to improve deep learning classification of chest X-ray images by addressing data imbalance, resulting in high accuracy and recall for minority disease classes.

Contribution

The paper proposes E-LDAM, an improved loss function that adjusts margin based on class sample size, enhancing disease classification in imbalanced chest X-ray datasets.

Findings

Achieved 97.81% recall for COVID-19 class

Attained 95.26% overall accuracy in three-class classification

Demonstrated effectiveness on COVIDx CXR dataset

Abstract

Deep Learning (DL) approaches have gained prominence in medical imaging for disease diagnosis. Chest X-ray (CXR) classification has emerged as an effective method for detecting various diseases. Among these methodologies, Chest X-ray (CXR) classification has proven to be an effective approach for detecting and analyzing various diseases. However, the reliable performance of DL classification algorithms is dependent upon access to large and balanced datasets, which pose challenges in medical imaging due to the impracticality of acquiring sufficient data for every disease category. To tackle this problem, we propose an algorithmic-centric approach called Effective-Label Distribution Aware Margin (E-LDAM), which modifies the margin of the widely adopted Label Distribution Aware Margin (LDAM) loss function using an effective number of samples in each class. Experimental evaluations on the COVIDx CXR dataset focus on Normal, Pneumonia, and COVID-19 classification. The experimental results demonstrate the effectiveness of the proposed E-LDAM approach, achieving a remarkable recall score of 97.81% for the minority class (COVID-19) in CXR image prediction. Furthermore, the overall accuracy of the three-class classification task attains an impressive level of 95.26%.