Artificial Intelligence Based Detection of Tuberculosis

• Author(s): Soni Yogesh Chaurasiya ; Dr. Santosh Singh ; Rimsy Dua ; Shreya Koli
• Paper ID: 1705475
• Page: 68-72
• Published Date: 03-02-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 8 February-2024

Abstract

Artificial intelligence (AI)-based methods in medical diagnostics have demonstrated impressive promise for tuberculosis (TB) early identification. In order to create a reliable TB detection system, this study combines the techniques of Random Forest. A deep learning framework for analyzing chest X-rays. The random forest classifiers are then trained using the obtained features, improving the model's understanding and accuracy. This strategy aims to increase the sensitivity and specificity of tuberculosis detection by utilizing the efficacy of deep learning and ensemble approaches. This will enable rapid and precise diagnoses, which are essential for restricting the disease's spread.

Keywords

Random Forest, Health, Tuberculosis, Detection, Chest X-rays, Computer Aided Diagnosis, Classification, Chest CT Scans, Healthcare.

Citations

IRE Journals:
Soni Yogesh Chaurasiya , Dr. Santosh Singh , Rimsy Dua , Shreya Koli "Artificial Intelligence Based Detection of Tuberculosis" Iconic Research And Engineering Journals Volume 7 Issue 8 2024 Page 68-72

IEEE:
Soni Yogesh Chaurasiya , Dr. Santosh Singh , Rimsy Dua , Shreya Koli "Artificial Intelligence Based Detection of Tuberculosis" Iconic Research And Engineering Journals, 7(8)