EHNN-PKC: Integrated Neural Network-Point Kernel Classification for Lung Cancer Detection

(E-pub Ahead of Print)

Author(s): Mercy Helen S*, Thangavel P..

Journal Name: Current Medical Imaging

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Abstract:

Background: A Computer Assisted Diagnosis (CAD) is the recently used framework that supports for the early detection and diagnosis of the tumor affected parts of the human body. The diversities in the shape, image intensities and the abnormalities make the manual approaches as the challenging one during the reading process of chest Computed Tomography (CT) scans. The lack of image clarity and the loss of edge information during the filtering process reduce the accuracy of segmentation and detection of tumor regions in the lung images. Discussion: To solve these issues, this paper proposes the integrated framework of Enhanced Hopfield Neural Network with the Point Kernel-based Classifier (PKC) for lung tumor detection. Initially, the pixel-intensity-based filtering removes the noise present in the image with edge information preserving capability. After that, the lung region is extracted from the preprocessed image with the use of proposed EHNN. Sequentially, the Local Tetra Patterns (LTrP) is employed to extract the patterns for describing the image information clearly. Finally, the Point Kernel-based Classifier (PKC) is used with the LTrP features for accurately classifying the normal and tumor affected portions. Conclusion: Moreover, the proposed EHNN-PKC is compared with the existing techniques by using various measures for analyzing the effectiveness of EHNN-PKC.

Keywords: Background Normalization, Filtering, Hopfield Neural Network (HNN), Kernel-based Classification, Local Tetra Pattern (LTrP)

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Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1573405614666180713114746
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