Lung Cancer Diagnosis from Ct Images Based on Pre-Processing and Ensemble Learning
Denny Dominic1, K. Balachandran2
1Denny Dominic, Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India.
2K. Balachandran, H.O.D. Department of Computer Science and Engineering, Christ (Deemed to be University), Bengaluru, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 10294-10297 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4519118419/2019©BEIESP | DOI: 10.35940/ijrte.D4519.118419
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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: Lung cancer is one of the dreaded diseases. Early detection is highly recommended to save the lives of the people. If the early detection is focused, we can reduce the mortality rate and increase the life expectancy accuracy 13% of the all new cancer diagnosis and 24% of all cancer deaths. There are various methods to detect the lung cancer from x-rays and CT images but the CT images are preferred. The medical images are always preferred to get better results on the same disease. The proposed method here will discuss about how the Watershed segmentation can be applied to the pre-processing of CT scans. The results are used for the deep learning methods so as to get the more accuracy from more image scans. The results are then used for the verification by the medical examiner for the validity of the results. The major pre-processing is done by using Median Filter, Gabor Filter and Watershed segmentation. In this research work we will discuss how the image manipulation can be done to achieve better results from the CT images through various image processing methods. The construction of the proposed method will include smoothing of the images with median filters, enhancement of the image and finally segmentation of the images.
Keywords: Metastasis, IMS, Watershed Segmentation, ROI, Threshold, CT Morphologic.
Scope of the Article: Image Processing and Pattern Recognition.