Author(s): J.Prassanna, L.Jani Anbarasi, Rukmani.P, Christy Jackson.J, B.Rajesh, R.Manikandan

Email(s): srmanimt75@gmail.com

DOI: 10.52711/0974-360X.2022.00760   

Address: J.Prassanna1, L.Jani Anbarasi1, Rukmani.P1, Christy Jackson.J1, B.Rajesh2, R.Manikandan3
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai.
2Department of Mathematics, University College of Engineering, Pattukkottai, 614701, India.
3School of Computing, SASTRA Deemed University, Thanjavur, India.
*Corresponding Author

Published In:   Volume - 15,      Issue - 10,     Year - 2022


ABSTRACT:
Medical Image Processing plays a major role in optimized identification of various diseases. In many parts of the world, tuberculosis is a serious health problem. Even in today's environment, diagnosing tuberculosis (TB) is difficult. The mortality role of those affected with TB is high due to the undiagnosed and untreated nature. Early detection of tuberculosis (TB) using X-rays of the lungs and classification to assist the treatments needed to improve their day-to-day routines. Early identification of the TB the lung X rays are segmented using Particle Swarm Optimization scheme. Features are extracted from the segmented lung Region of Interest using the texture and the shape features. Prominent Features are identified using a genetic algorithm. The reduced set of features are classified using neural network thus enabling the images to be classified as Normal or Abnormal. The accuracy, recall and, sensitivity achieved by the methodology have been reported in this paper.


Cite this article:
J.Prassanna, L.Jani Anbarasi, Rukmani.P, Christy Jackson.J, B.Rajesh, R.Manikandan. CNN based Framework for intelligent Diagnosis of Tuberculosis using Chest Radiographs. Research Journal of Pharmacy and Technology2022; 15(10):4529-2. doi: 10.52711/0974-360X.2022.00760

Cite(Electronic):
J.Prassanna, L.Jani Anbarasi, Rukmani.P, Christy Jackson.J, B.Rajesh, R.Manikandan. CNN based Framework for intelligent Diagnosis of Tuberculosis using Chest Radiographs. Research Journal of Pharmacy and Technology2022; 15(10):4529-2. doi: 10.52711/0974-360X.2022.00760   Available on: https://rjptonline.org/AbstractView.aspx?PID=2022-15-10-33


REFERENCES:
1.    Lalit Kumar, Rajan, Vivek Sharma. Tuberculosis: A Brief Overview. Asian J. Pharm. Res. 2(2): April-June 2012; Page 59-62. https://asianjpr.com/AbstractView.aspx?PID=2012-2-2-4
2.    Soha Patel. A Study to assess the effectiveness of structured teaching programme on knowledge regarding prevention of Tuberculosis among the adult people in selected rural area at Gothava. Asian J. Nursing Education and Research. 2020; 10(3): 339-342. doi: 10.5958/2349-2996.2020.00072.5
3.    Kiran Madhawai, Dinesh Rishipathak, Santosh Chhajed, Sanjay Kshirsagar. Predicting the Anti-Inflammatory Activity of Novel 5-Phenylsulfamoyl-2-(2-Nitroxy) (Acetoxy) Benzoic acid derivatives using 2D and 3D-QSAR (kNN-MFA) Analysis. Asian J. Res. Pharm. Sci. 2017; 7(4): 227-234. doi: 10.5958/2231-5659.2017.00036.4
4.    Bindu Sree Koduru, Akshay R. Shinde, P. Jaya Preeti, K. Pavan Kumar, R. Rajavel, T. Sivakumar. Synthesis, Characterization, Anti-tubercular, Analgesic and Anti-Inflammatory Activities of New 2- Pyrazoline Derivatives. Asian J. Pharm. Tech. 2(2): April-June 2012; Page 47-50. doi: 10.5958/2231–5713
5.    Sagavkar Sandhyarani R, Devkar Swati R. Tuberculosis: A Review. Asian J. Pharm. Res. 2018; 8(3): 191-194. doi: 10.5958/2231-5691.2018.00033.3.
6.    Josmy Abraham. Effect of Planned Teaching Programme on knowledge and practices in relation to selected aspects of Tuberculosis among patients diagnosed with Tuberculosis. Int. J. Nur. Edu. and Research. 2018; 6(4):404-410. doi: 10.5958/2454-2660.2018.00098.4.
7.    R. Nuziba Begum.Dipsomania. Asian J. Nur. Edu. and Research 1(3): July-Sept. 2011; Page 98. https://ajner.com/AbstractView.aspx?PID=2011-1-3-11
8.    Kansal, Anita Rani, et al. "A study to assess learning need, knowledge and attitude of nurses regarding tuberculosis care under RNTCP in two tertiary care tuberculosis institutions of Delhi, India." Asian Journal of Nursing Education and Research 4.1 (2014): 30-34. https://ajner.com/AbstractView.aspx?PID=2014-4-1-7
9.    Periadurachi Kumar, Dr. K.R. John. Impact of need-based training of healthcare workers on their knowledge and practice regarding case finding under RNTCP at selected tuberculosis unit’s of primary health centres, Bangalore. Asian J. Nursing Education and Research. 2020; 10(2):145-153. doi: 10.5958/2349-2996.2020.00032.4
10.    Iram Khan. A study to assess the effectiveness of planned Teaching Programme regarding knowledge and prevention of Tuberculosis among adult in ghogha area of Bhavnagar city. Asian J. Nursing Education and Research. 2020; 10(3): 343-346. doi: 10.5958/2349-2996.2020.00073.7
11.     Monesh O. Patil, Yogesh S. Mali, Paresh A. Patil, D. R. Karnavat. Development of Immunotherapeutic Nanoparticles for treatment of Tuberculosis. Asian J. Pharm. Res. 2020; 10(3):226-232. doi: 10.5958/2231-5691.2020.00039.8
12.    Navdeep Singh, Shivi Sondhi, Shammy Jindal, Vinay Pandit, Mahendra Singh Ashawat. Treatment and Management for patients with mild to severe Psoriasis: A Review. Asian J. Pharm. Res. 2020; 10(4):286-292. doi: 10.5958/2231-5691.2020.00049.0
13.    Kumar, G. Satheesh, et al. "Extraction, Phytochemical Studies and In-Vitro Screening of the Leaves and Flowers of Crossandra infundibuliformis against Mycobacterium tuberculosis." Asian Journal of Research in Pharmaceutical Science 8.4 (2018): 247-252. . doi: 10.5958/2231-5659.2018.00041.3
14.    Shu Zhang. Zhi Xu. Chuan Gao. Qing-Cheng Ren. Le Chang. Zao-Sheng Lv. Lian-Shun Feng. Triazole derivatives and their anti-tubercular activity. Eur J Med Chem. 2017; 138:501-513. doi: 10.1016/j.ejmech.2017.06.051
15.    Sr. Prabha Grace. A Personal Experience with a Tuberculosis patient: A Case Report. Int. J. of Advances in Nur. Management. 2018; 6(4): 290-292. doi:10.5958/2454-2652.2018.00066.5
16.    Akoijam Sangita Devi. Knowledge and Attitude of Patients Regarding Pulmonary Tuberculosis. Int. J. Nur. Edu. and Research 3(2): April-June, 2015; Page 201-208. https://ijneronline.com/AbstractView.aspx?PID=2015-3-2-20
17.     Fatima, Noor, et al. "Knowledge on prevention of pulmonary tuberculosis patients among the family members of patients who are admitted in selected Hospital in Bareilly, Uttar Pradesh with self instructional module." International Journal of Nursing Education and Research 8.4 (2020): 512-516. doi:10.5958/2454-2660.2020.00114.3
18.    Jawahar, Malathy, et al. "Diabetic Foot Ulcer Segmentation using Color Space Models." 2020 5th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2020. doi:10.1109/ICCES48766.2020.9138024
19.    Aleksandr Zotin. Yousif Hamad. Konstantin Simonov. Mikhail Kurako. Lung boundary detection for chest X-ray images classification based on GLCM and probabilistic neural networks. Procedia Computer Science. 2019; 159:1439-1448. doi:10.1016/j.procs.2019.09.314
20.    Jawahar, Malathy, et al. "Vision based inspection system for leather surface defect detection using fast convergence particle swarm optimization ensemble classifier approach." Multimedia Tools and Applications (2020): 1-33. doi:10.1007/s11042-020-09727-3
21.    Jaeger, S. "Karargyris A Candemir S Folio L Siegelman J Callaghan FM Xue Z Palaniappan K Singh RK Antani SK Thoma GR Automatic tuberculosis screening using chest radiographs." IEEE Trans. Med. Imaging 33.2 (2014): 233. doi: 10.1109/TMI.2013.2284099
22.    Scarpiniti, Michele, et al. "A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images." Applied Sciences 11.19 (2021): 8867. doi:10.3390/app11198867
23.    Theresa, M. Mercy, and V. Subbiah Bharathi. "CAD for lung nodule detection in chest radiography using complex wavelet transform and shearlet transform features." Indian Journal of Science and Technology. 9.1 (2016): 1-12. doi:10.17485/ijst/2016/v9i1/75243
24.    S. Pushparani., V. Vallinayagam, A. Chandra Sekar, L. Jani Anbarasi, “Automated Classification of Tuberculosis by PSO based Machine Learning using Chest Radiographs”, International Journal of Engineering Research & Technology (IJERT). ol: 5,Iss:10, 405-411. doi: 10.17577/IJERTV5IS100275E
25.    A. Dawoud, “Fusing shape information in lung segmentation in chest radiographs,” Image Anal. Recognit. , pp. 70–78, 2010. doi: 10.1007/978-3-642-13775-4_8
26.    Badarudin Hakim and Basari. Tuberculosis detection analysis using texture features on CXRs images. AIP Conference Proceedings 2092, 040001 (2019); doi:10.1063/1.5096734
27.    Stefan Jaeger etal, “ Automatic Tuberculosis Screening Using Chest Radiographs”, IEEE Transactions On Medical Imaging, Vol. 33, No. 2, February 2014, PP. 233. doi: 10.1109/TMI.2013.2284099
28.    R. Shen, I. Cheng, and A. Basu, “A hybrid knowledge-guided detection technique for screening of infectious pulmonary tuberculosis from chest radiographs,” IEEE Trans. Biomed. Eng., vol. 57, no. 11, pp. 2646–2656, Nov. 2010. doi: 10.1109/TBME.2010.2057509
29.    Anbarasi, L. Jani, Modigari Narendra, and G. S. Mala. "Cheating prevention using genetic feature based key in secret sharing schemes." International Symposium on Security in Computing and Communication. Springer, Berlin, Heidelberg, 2014.. doi: 10.1007/978-3-662-44966-0_11
30.    Po-Yen Ko, Shi-Dou Lin, Shih-Te Tu, Ming-Chia Hsieh, Shih-Li Su, Shang-Ren Hsu, Yu-Cheng Chen, “ High diabetes mellitus prevalence with increasing trend among newly-diagnosed tuberculosis patients in an Asian population: A nationwide population-based study” Primary Care Diabetes, Volume 10, Issue 2, April 2016, Pages 148-155. doi: 10.1016/j.pcd.2015.09.005
31.    Modigari Narendra, L. Jani Anbarasi, S. Graceline Jasmine, J. Prassanna, R. Prabhakaran,” Breast Cancer Detection Using Histology Images: A Survey”, Journal of Advanced Research in Dynamical and Control Systems, Vol 12, pp.561-565,2020. doi: 10.5373/JARDCS/V12SP7/20202140
32.    SenthilKumar, AL Prajoth, et al. "Breast cancer Analysis and Detection in Histopathological Images using CNN Approach." Proceedings of International Conference on Intelligent Computing, Information and Control Systems. Springer, Singapore, 2021. doi:10.1007/978-981-15-8443-5_27

Recomonded Articles:

Research Journal of Pharmacy and Technology (RJPT) is an international, peer-reviewed, multidisciplinary journal.... Read more >>>

RNI: CHHENG00387/33/1/2008-TC                     
DOI: 10.5958/0974-360X 

1.3
2021CiteScore
 
56th percentile
Powered by  Scopus


SCImago Journal & Country Rank


Recent Articles




Tags


Not Available