Mil based lung CT-image classification using CNN
Tuberculosis (TB) is diagnosed using clinical settings through which the specimen taken as of the patient is examined. The availability of Mycobacterium tuberculosis bacteria (MTB) in that specimen confirms the existence of TB. The other examinations strongly recommend that, TB in the diagnosis, one cannot confirm it. A simple skin test is the most generally utilized diagnostic equipment for TB, though blood tests are becoming more common. A small quantity of a substance termed PPD (Post-Partum Depression) tuberculin is injected below the skin of one’s inside forearm. Sometimes, false negative treatment may also results. Thus several upcoming techniques are introduced to cure the TB. It is based upon the CAD system that deals with the problem of TB detection on CXR. It is maintained by the training dataset. The drawback that occurs on the previous model is that its inherent features are uncertain. The novel 3 methods implemented in this research are i) Lung Segment, ii) Texture Feature Extraction as well as iii) Pixel classification. Along with these, miSVM+PEDD (multiple instances Support Vector Machine + Probability Estimation and Data Discarding) is used for the segmentation process. Thus the model should be evaluated by implementing some recent features of the Multiple Instance Learning (MIL). The Improved Algorithm is deployed by training a MIL classifier which builds from other machine learning (ML), Active Learning (AL) and 1-class classification approaches. Here, the uncertainty intrinsic to a MIL pixel classifier is diminished while minimizing the labeling exertions. The uses of AL are centered on the image and text categorization. The resulting lung likelihood map is post-processed to obtain binary segmentation. Here, the MIL is integrated with the AL Model. The results are analyzed by contrasting the proposed system with the other prevailing techniques to prove the dominance of the proposed one.
KeywordsTuberculosis CAD CNN Bag of words miSVM+PEDD MIL
Compliance with ethical standards
Our work is not funded by any agencies or organization.
Conflict of interest
None of the author received fund from any agencies or committee or organization.
- 1.Saraswati LD, P Ginandjar, B Widjanarko, RA Puspitasari. Epidemiology of child tuberculosis (a cross-sectional study at pulmonary health center Semarang City, Indonesia). In IOP conference series: earth and environmental science, vol. 116, no. 1, p. 012081. IOP publishing, 2018; 116: 012081.Google Scholar
- 2.Dinesh Jackson Samuel R, Rajesh Kanna B. Tuberculosis (TB) detection system using deep neural networks. Neural Comput & Applic. 2018:1–13.Google Scholar
- 3.Dinesh Jackson Samuel R, Rajesh Kanna B. Cybernetic microbial detection system using transfer learning. Multimed Tools Appl. 2018.Google Scholar
- 4.World Health Organization. Chest radiography in tuberculosis detection: summary of current WHO recommendations and guidance on programmatic approaches. No. WHO/HTM/TB/2016.20. World Health Organization. 2016.Google Scholar
- 6.W Zhao, Y Kong, Z Ding, Yun Fu. Deep active learning through cognitive information parcels. In Proceedings of the 2017 ACM on multimedia conference. 2017; 952–960. ACM.Google Scholar
- 7.GM Fung, Dundar M, B Krishnapuram, RB Rao. Multiple instance learning for computer aided diagnosis, In Advances in neural information processing systems. 2007; 425–432.Google Scholar
- 8.BalaAnand M, Karthikeyan N, Karthik S. Designing a framework for communal software: based on the assessment using relation modelling. Int J Parallel Prog. 2018. https://doi.org/10.1007/s10766-018-0598-2.
- 10.M BalaAnand, S Sankari, R Sowmipriya, S Sivaranjani. Identifying Fake User’s in Social Networks Using Non Verbal Behavior", International Journal of Technology and Engineering System (IJTES). 7(2): 157–161.Google Scholar
- 11.Maram B, Gnanasekar JM, Manogaran G, Balaanand M. Intelligent security algorithm for UNICODE data privacy and security in IOT. SOCA. 2018. https://doi.org/10.1007/s11761-018-0249-x.
- 12.M BalaAnand, N Karthikeyan, S Karthick, CB Sivaparthipan. Demonetization: a visual exploration and pattern identification of people opinion on tweets, International Conference on Soft-computing and Network Security (ICSNS), Coimbatore, India. 2018; 1–7. doi: https://doi.org/10.1109/ICSNS.2018.8573616.
- 18.Y Yang, D-C Zhan, Y Jiang. Learning by actively querying strong modal features, In IJCAI, 2016; 2280–2286.Google Scholar
- 19.Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, Ronald M. Summers, “three aspects on using convolutional neural networks for computer-aided detection in medical imaging”, In Deep learning and convolutional neural networks for medical image computing. Springer, Cham. 2017; 113–136.Google Scholar
- 21.Zheng Wang, and Jieping Ye, “Querying discriminative and representative samples for batch mode active learning." ACM Trans Knowl Discov Data (TKDD), vol. 9, no. 3, pp. 17, 2015.Google Scholar
- 22.Stuart Andrews, Ioannis Tsochantaridis, and Thomas Hofmann. Support vector machines for multiple-instance learning. In Advances in neural information processing systems. 2003; 577–584.Google Scholar
- 25.Z-J Zha, X-S Hua, T Mei, J Wang, G-J Qi, Z Wang. Joint multi-label multi-instance learning for image classification. In Computer vision and pattern recognition, 2008. CVPR 2008. IEEE conference on, 2008; 1–8.Google Scholar
- 26.K Anupriya, R Gayathri, M Balaanand, CB Sivaparthipan. Eshopping scam identification using machine learning. International Conference on Soft-computing and Network Security (ICSNS), Coimbatore, India. 2018; 1–7. doi: https://doi.org/10.1109/ICSNS.2018.8573687.
- 27.T Cooke. Detection and classification of objects in synthetic aperture radar imagery, No. DSTO-RR-0305. Defence science and technology organisation Edinburgh (Australia) intelligence surveillance and reconnaissance div. 2006.Google Scholar