Enhanced Tuberculosis Bacilli Detection using Attention-Residual U-Net and Ensemble Classification

TL;DR

This paper presents a hybrid deep learning approach combining an attention-residual U-Net for precise TB bacilli segmentation and an ensemble classifier for accurate detection, improving automation and performance over existing methods.

Contribution

It introduces an enhanced U-Net with attention and residual connections for better segmentation, combined with an ensemble classifier for improved TB bacilli detection.

Findings

Superior segmentation performance on multiple datasets

Higher classification accuracy than existing methods

Enhanced automation in TB bacilli detection

Abstract

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a critical global health issue, necessitating timely diagnosis and treatment. Current methods for detecting tuberculosis bacilli from bright field microscopic sputum smear images suffer from low automation, inadequate segmentation performance, and limited classification accuracy. This paper proposes an efficient hybrid approach that combines deep learning for segmentation and an ensemble model for classification. An enhanced U-Net model incorporating attention blocks and residual connections is introduced to precisely segment microscopic sputum smear images, facilitating the extraction of Regions of Interest (ROIs). These ROIs are subsequently classified using an ensemble classifier comprising Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boost (XGBoost), resulting in an accurate identification of bacilli within the images. Experiments conducted on a newly created dataset, along with public datasets, demonstrate that the proposed model achieves superior segmentation performance, higher classification accuracy, and enhanced automation compared to existing methods.