Shallow Cnn with Lstm Layer for Tuberculosis Detection in Microscopic Image
Anson Simon1, Vinaya Kumar R2, Sowmya V3, K P Soman4
1Anson Simon, Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore (Tamil Nadu), India.
2Vinaya Kumar R, Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore (Tamil Nadu), India.
3Sowmya V, Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore (Tamil Nadu), India.
4K P Soman, Center for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore (Tamil Nadu), India.
Manuscript received on 22 April 2019 | Revised Manuscript received on 01 May 2019 | Manuscript Published on 07 May 2019 | PP: 56-60 | Volume-7 Issue-6S3 April 2019 | Retrieval Number: F1012376S19/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Tuberculosis or TB, a disease mainly affecting lungs is infected by bacterium mycobacterium tuberculosis and diagnosed by careful examination of microscopic images taken from sputum specimen. Diagnosis of disease using microscopy and computer vision methods are applied for many previous practical problems. Recently, deep learning is playing major role in computer vision applications producing remarkable performance. But, computational complexity always remains as an obstacle in the application of deep learning in many aspects. So in this paper, a shallow CNN with LSTM layer is used for detecting the tubercle bacillus, mycobacterium tuberculosis, from the microscopic images of the specimen collected from the patients. The specified model is producing better performance than state of the art model and also have reduced number of learnable parameters, which requires comparatively less computation than the existing model.
Keywords: CNN; Deep Learning; LSTM Layer; Tubercle Bacillus; Tuberculosis.
Scope of the Article: Image Security