Artificial Intelligence Based Detection of Tuberculosis

References

[1] Puttagunta MK, Ravi S. Detection of Tuberculosis based on Deep Learning based methods. InJournal of Physics: Conference Series 2021 Feb 1 (Vol. 1767, No. 1, p. 012004). IOP Publishing.

[2] Klassen VI, Safin AA, Maltsev AV, Andrianov NG, Morozov SP, Vladzymyrskyy AV. AI-based screening of pulmonary tuberculosis: diagnostic accuracy. Journal of eHealth Technology and Application. 2018 Nov;16(1):28-32.

[3] Duong LT, Le NH, Tran TB, Ngo VM, Nguyen PT. Detection of tuberculosis from chest X-ray images: Boosting the performance with vision transformer and transfer learning. Expert Systems with Applications. 2021 Dec 1;184:115519.

[4] Fati SM, Senan EM, ElHakim N. Deep and hybrid learning technique for early detection of tuberculosis based on X-ray images using feature fusion. Applied Sciences. 2022 Jul 14;12(14):7092.

[5] Kiruthika SU, Raja SK, Balaji V, Raman CJ, Arumugam SD. Detection of tuberculosis in chest X-rays using U-net architecture. International Journal of Innovative Technology and Exploring Engineering, ISSN. 2019:2278- 3075.

[6] Sathitratanacheewin S, Sunanta P, Pongpirul K. Deep learning for automated classification of tuberculosis-related chest X-Ray: dataset distribution shift limits diagnostic performance generalizability. Heliyon. 2020 Aug 1;6(8).

[7] Sahlol AT, AbdElaziz M, Tariq Jamal A, Damaševičius R, Farouk Hassan O. A novel method for detection of tuberculosis in chest radiographs using artificial ecosystem-based optimisation of deep neural network features. Symmetry. 2020 Jul 8;12(7):1146.

[8] Showkatian E, Salehi M, Ghaffari H, Reiazi R, Sadighi N. Deep learning-based automatic detection of tuberculosis disease in chest X-ray images. Polish journal of radiology. 2022 Feb 28;87(1):118-24.

[9] Abubakar M, Shah I, Ali W. Classification of Pneumonia and Tuberculosis from Chest Xrays. arXiv preprint arXiv:2103.14562. 2021 Mar 25.

[10] Naeem MM, Anwar S, Abid A, Ahmed Z. Machine Vision based Computer -Aided Detection of Pulmonary Tuberculosis using Chest X-Ray Images. Pakistan Journal of Engineering and Technology. 2020 Dec 23;3(03):63-8.

[11] Soares TR, de Oliveira RD, Liu YE, Silva Santos AD, Santos PC, Monte LR, Oliveira LM, Park CM, Hwang EJ, Andrews JR, Croda J. Evaluation of chest X-Ray with automated interpretation algorithms for mass tuberculosis screening in prisons. medRxiv. 2021 Dec 29:2021-12.

[12] Ahmed IA, Senan EM, Shatnawi HS, Alkhraisha ZM, Al-Azzam MM. Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features. Diagnostics. 2023 Feb 20;13(4):814.

@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},
  }