Deep Learning for Tuberculosis Detection: Enhancing Di-agnostic with Chest X-Rays

Computer Science

The application of deep learning in medical imaging, particularly for tuberculosis (TB) de-tection using chest X-rays, represents a transformative approach to address the global health challenge of TB. With millions of new cases and deaths annually, especially in low- and middle-income countries, traditional diagnostic methods are slow, labor-intensive, and prone to human error. The use of artificial intelligence (AI), particularly deep learning tech-niques like Convolutional Neural Networks (CNNs), has the potential to significantly im-prove diagnostic accuracy and speed. Recent advancements, such as stochastic learning-based neural networks, DenseNet architecture enhancements, and the fusion of CNNs with traditional machine learning models, enable AI to identify subtle patterns in chest X-rays that are often difficult for human observers. These advances, including stochastic behavior in neural networks and ensemble methods, have proven to improve generalization and re-duce bias, leading to more accurate TB detection across diverse patient demographics. De-spite significant progress, challenges remain in deploying these AI systems in real-world clinical settings, including data scarcity, model interpretability, and the need for robust, high-quality data. This paper explores the current advancements in deep learning for TB detection, highlights the potential impact on public health, and outlines future research di-rections aimed at further improving the technology's efficiency and scalability.

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