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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Monappa Rama, Kushalatha
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Computer Science & IT Engineering

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Analysis of tuberculosis detection using deep learning technique and explainable artificial intelligence Srinivas, Shashikiran; Patil, Kavita Avinash; Monappa Rama, Kushalatha; Venkateshlu, Sudha; Muthuswamy, Jayanthi; Babu Narayanappa, Srinivas
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1623-1631

Abstract

Tuberculosis (TB) affects the health of many individuals and is still a prime worldwide health concern despite having so many advanced treatments, as it still lacks technical advancement in its treatment and diagnosis. Accuracy in identification and early detection is essential to reduce the spread and improve treatment outcomes. Traditional methods of diagnosis, such as sputum microscopy and culture, are labor-dependent and subject to human mistakes as it is done by lab technicians. Recent improvements in deep learning have demonstrated significant potential for enhancing and automating diagnostic accuracy. Our research proposes a deep learning based technique that detects TB from chest X-rays after image processing techniques like augmentation. After training on big data, our model pulls off an astonishing accuracy of 97.42% and a loss of 7.17%, outperforming traditional methods. The model uses convolutional neural network (CNN) as a base and transfer learning method, like DenseNet-121, and explainable artificial intelligence (XAI) technique, like Grad-CAM, to recognize TB related patterns effectively and with low false positives. This approach has the ability to revolutionize the diagnosis of TB and offer more dependable, scalable, and timely solutions to healthcare systems worldwide.
Co-Authors Babu Narayanappa, Srinivas Muthuswamy, Jayanthi Patil, Kavita Avinash Srinivas, Shashikiran Venkateshlu, Sudha