Deep Learning Based Approach Classification Of Tuberculosis: A Comprehensive Image Dataset Analysis

Abstract

Tuberculosis (TB) remains a significant global health threat, responsible for millions of deaths annually. Traditional diagnostic methods, such as sputum smear microscopy andchestX- rays,areoftentime-consuming,subjective,andlimitedindetectingearly- stage TB or differentiating it from other lung diseases. With the rise of deep learning technologies, automated classification of TB using medical imaging presents a promising solution for improving diagnosis accuracy and speed. Historically, medical professionals have relied on manual interpretation of chest X-rays, which is prone to errors due to variability in expertise and image quality. This challenge, coupled with limited resources in many high-burden regions, underscores the need for a more reliable, automated system. In this research, we propose a deep learning-based approach for classifying TB using chest X-ray images. Leveraging convolutional neural networks (CNNs), this system can automatically detect TB- related features in medical images with high accuracy. The proposed model offers significant improvements over traditional methods by providing faster, consistent, and more accurate results, especially in resource-limited settings. This data-driven approach holds great potential in aiding healthcare professionals in TB diagnosis and reducing the disease's global burden.

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