A Deep Learning Approach for Enhancing Tuberculosis Classification Leveraging Optimized Sequential AlexNet (OSAN)

Abstract

In this research paper, we present an innovative tuberculosis (TB) classification model built upon the well-established AlexNet architecture, with a primary emphasis on its outstanding performance in the realm of TB detection. Tuberculosis remains a formidable challenge to global healthcare systems, particularly in resource-limited settings. Timely and accurate diagnosis is of paramount importance for the effective management and containment of this disease. Our approach entails meticulous architectural refinements and rigorous training on a diverse dataset encompassing a wide spectrum of TB-related symptoms. This comprehensive training ensures the model’s adaptability and resilience in addressing real-world diagnostic complexities. The central objective of our OSAN model is to categorize medical images into two crucial groups: ”normal” and ”TB-infected.” The outcomes achieved are truly noteworthy, with a classification accuracy rate of 99.67%. This exceptional level of accuracy underscores the model’s potential to bring about transformative changes in TB diagnostics. It holds the promise of early identification, facilitating prompt intervention, and ultimately leading to improved patient outcomes. Our research contributes to the overarching objective of enhancing patient care and supporting global health initiatives. By providing a reliable and accessible tool for TB diagnosis, our model has the potential to make a significant impact in the battle against this persistent global health menace.