Multi-axis transformer based U-Net with class balanced ensemble model for lung disease classification using X-ray images.

Abstract

Background: Chest X-rays are an essential diagnostic tool for identifying chest disorders because of its high sensitivity in detecting pathological anomalies in the lungs. Classification models based on conventional Convolutional Neural Networks (CNNs) are adversely affected due to their localization bias. Objective: In this paper, a new Multi-Axis Transformer based U-Net with Class Balanced Ensemble (MaxTU-CBE) is proposed to improve multi-label classification performance. Methods: This may be the first attempt to simultaneously integrate the benefits of hierarchical Multi-Axis Transformer into the encoder and decoder of the traditional U-shaped structure for improving the semantic segmentation superiority of lung image. Results: A key element of MaxTU-CBE is the Contextual Fusion Engine (CFE), which uses the self-attention mechanism to efficiently create global interdependence between features of various scales. Also, deep CNN incorporate ensemble learning to address the issue of class unbalanced learning. Conclusions: According to experimental findings, our suggested MaxTU-CBE outperforms the competing BiDLSTM classifier by 1.42% and CBIR-CSNN techniques by 5.2% in multi-label classification performance.