Deep Learning Based Depthwise Separable Model For Effective Diagnosis And Classification of 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 September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 1808-1819 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1439109119/2019©BEIESP | DOI: 10.35940/ijeat.A1439.109119
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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: Lung cancer is a serious illness which leads to increased mortality rate globally. The identification of lung cancer at the beginning stage is the probable method of improving the survival rate of the patients. Generally, Computed Tomography (CT) scan is applied for finding the location of the tumor and determines the stage of cancer. Existing works has presented an effective diagnosis classification model for CT lung images. This paper designs an effective diagnosis and classification model for CT lung images. The presented model involves different stages namely pre-processing, segmentation, feature extraction and classification. The initial stage includes an adaptive histogram based equalization (AHE) model for image enhancement and bilateral filtering (BF) model for noise removal. The pre-processed images are fed into the second stage of watershed segmentation model for effectively segment the images. Then, a deep learning based Xception model is applied for prominent feature extraction and the classification takes place by the use of logistic regression (LR) classifier. A comprehensive simulation is carried out to ensure the effective classification of the lung CT images using a benchmark dataset. The outcome implied the outstanding performance of the presented model on the applied test images.
Keywords: Lung cancer; CT images; Feature extraction; Classification; Segmentation