Artificial Intelligence Based Detection of Tuberculosis

@article{1705475,
      author = {Soni Yogesh Chaurasiya, Dr. Santosh Singh, Rimsy Dua, Shreya Koli},
      title = {Artificial Intelligence Based Detection of Tuberculosis},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {8},
      pages = {68-72},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705475.pdf},
      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. },
      month = {February},
  }