Pulmonary Nodule Classification in Thoracic CT Images using Random Forest Algorithm
Utkarsh Shukla1, Kshitij Srivastava2, Aarav Bhati3, M. Jasmine Pemeena Priyadarsini4, A.Jabeena5, G.K.Rajini6
1Utkarsh Shukla*, School of Electronics Engineering, Vellore Institute of Technology, Vellore.
2Kshitij Srivastava, School of Electronics Engineering, Vellore Institute of Technology, Vellore.
3Aarav Bhati, School of Electronics Engineering, Vellore Institute of Technology, Vellore.
4M. Jasmine Pemeena Priyadarsini, School of Electronics Engineering, Vellore Institute of Technology, Vellore.
5A.Jabeena, School of Electronics Engineering, Vellore Institute of Technology, Vellore.
6G.K.Rajini, School of Electrical Engineering, Vellore Institute of Technology, Vellore.
Manuscript received on September 21, 2019. | Revised Manuscript received on October 05, 2019. | Manuscript published on October 30, 2019. | PP: 3716-3720 | Volume-9 Issue-1, October 2019 | Retrieval Number: F8643088619/2019©BEIESP | DOI: 10.35940/ijeat.F8643.109119
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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: In this paper, an automatic classification of thoracic pulmonary nodules with Computed Tomography Image as input is performed. We can crisply classify the nodules into two categories: Benign and Malignant. Benign nodules are the ones which do not cause any harm and even if they do, the impact is negligible. Malignant Nodules are the ones which, if not detected on time can cause severe damage to a person, even resulting in death. Henceforth, detection at early stage of lung cancer is critical. We plan to perform our analysis in 4 steps. Firstly, a noise free CT image is obtained after preprocessing. Then, we apply the improved Random Walker algorithm to perform regionbased segmentation, resulting in generation of foreground and background seeds. The next step is to bring out important features of the segments. The features can be intensity, texture and geometry based. Finally we used an improved Random Forest method to generate classification trees, comprising of different class labels. Using RF Algorithm, we predict the accurate class label which corresponds to a particular type of nodule and the stage of cancer that it has developed.
Keywords: Benign, Malignant, Random, Walker, Forest, Preprocessing, Segmentation, Classification.