Segmentation of Lung Ct Images using Cascaded Fully Convolutional Neural Networks
J. Maruthi Nagendra Prasad1, M. Vamsi Krishna2
1J Maruthi Nagendra Prasad, Research Scholar, Department of Computer Science & Engineering, Centurion University of Technology and Management, Paralakhemundi, Orissa
2Dr. M, Vamsi Krishna, Professor, Department of Computer Science and Engineering, Chaitanya Engineering College, Kakinada
Manuscript received on 16 March 2019 | Revised Manuscript received on 23 March 2019 | Manuscript published on 30 July 2019 | PP: 5472-5474 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3825078219/19©BEIESP | DOI: 10.35940/ijrte.B3825.078219
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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: Interpretation of CT Lung images by the radiologist can be enhanced to a greater extent by automatic segmentation of nodules. The efficiency of this interpretation depends on the completeness and non-ambiguousness of the CT Lung images. Here, a fully automatic cascaded basis was proposed for CT Lung image segmentation. In this proposal a customized FCN was used feature extractions exploration from many visual scales and differentiate anatomy with a thick forecast map. Widespread experimental outcomes demonstrate that this technique can address the incompleteness in boundary and this technique can achieve best accuracy in segmentation of Lung CT Images when compared to other techniques which address the same area.
Keyword: CT Lung Image, Segmentation, Fully Convolutional Neural Networks, Cascading
Scope of the Article: Image Security