International Journal of Science, Engineering and TechnologyAuthors: Mulsa Venkata Sravya, Associate Professor Mrs.M.Radhika
Abstract: Due to a lack of technical improvements in diagnosis and treatment, tuberculosis (TB) continues to impact millions of people worldwide, despite the availability of highly effective medicines. Accurate identification and early diagnosis are crucial for reducing the spread and enhancing treatment results. Traditional diagnostic methods, such as sputum microscopy and culture, are labour-intensive and susceptible to human error since they are carried out by laboratory professionals. The field of deep learning (DL) has lately seen remarkable progress, and it shows great promise for improving and automating the accuracy of diagnoses. A DL-based approach to TB detection in chest X-rays is proposed in our work. Following training on a large dataset, our model outperforms traditional methods with a loss of 8.19 percent and an impressive accuracy of 97.32 percent. With convolutional neural networks (CNN) as its foundation and augmented with transfer learning (SqueezeNet) and explainable artificial intelligence (AI) methods like Grad-CAM, the model can accurately identify TB-related patterns while producing few false positives. More reliable, scalable, and rapid solutions for healthcare systems throughout the world may be possible with this approach, which would revolutionise TB diagnosis.
