Computer Aided Diagnosis System using Watershed Segmentation with Xception Based Classification Model for Lung CT Images
D. Jayaraj1, S. Sathiamoorthy2
1D. Jayaraj, Department of Computer Science & Engineering, Annamalai University, Chidambaram
2S. Sathiamoorthy, Tamil Virtual Academy, Chennai.
Manuscript received on October 16, 2019. | Revised Manuscript received on 24 October, 2019. | Manuscript published on November 10, 2019. | PP: 3625-3635 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4618119119/2019©BEIESP | DOI: 10.35940/ijitee.A4618.119119
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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: Early recognition and classification of pulmonary nodules by the use of computer-aided diagnosis (CAD) tools finds useful to reduce the death rate due to the illness of lung cancer. This paper devises a new CAD tool utilizing a segmentation based classification process for lung CT images. Initially, the input CT images are pre-processed by image enhancement and noise removal process. Then, watershed segmentation model is employed for the segmentation of the pre-processed images. Subsequently, the feature extraction process is carried out using Xecption model and random forest (RF) classifier is used of the identification of lung CT images as normal, benign or malignant. The use of RF model results to effective classification of the applied images. This model undergoes extensive experimentation against a benchmark lung CT image dataset and the results are investigated under several aspects. The obtained outcome pointed out the significant performance of the presented model over the compared methods.
Keywords: CT Image, Lung Cancer, Classification, Random forest, Xception.
Scope of the Article: Classification