Enhanced Tuberculosis Bacilli Detection using Attention-Residual U-Net and Ensemble Classification
This work addresses the need for more automated and accurate tuberculosis diagnosis, which is critical for global health, though it appears incremental as it combines established techniques like U-Net and ensemble classifiers.
The paper tackled the problem of detecting tuberculosis bacilli from microscopic sputum smear images by proposing a hybrid deep learning approach, achieving superior segmentation and classification accuracy compared to existing methods.
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.