Open AccessDeep Learning Based Depthwise Separable Model For Effective Diagnosis And Classification of Lung Ct Images
Author(s) -
D. Jayaraj,
S. Sathiamoorthy
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1439.109119
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , feature extraction , segmentation , histogram equalization , lung cancer screening , classifier (uml) , adaptive histogram equalization , stage (stratigraphy) , histogram , computed tomography , radiology , image (mathematics) , medicine , paleontology , biology
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.