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Current Computer-Aided Drug Design

Editor-in-Chief

ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

Research Article

Computer-Aided Detection System for the Classification of Non-Small Cell Lung Lesions using SVM

Author(s): Shruti Jain*

Volume 16, Issue 6, 2020

Page: [833 - 840] Pages: 8

DOI: 10.2174/1573409916666200102122021

Price: $65

Abstract

Introduction: Lung carcinoma is the most commonly cancer causing deaths throughout the world that mainly occurs due to smoking. Small cell lung cancer and Non-small cell lung cancer (NSCLC) are the two different types of Lung cancer. For the detection and classification of lung cancer, there are different techniques in the literature.

Methods: This paper emphasis on the three class classification of the Adenocarcinomas, Squamous cell carcinomas, and large cell carcinomas of NSCLC. For precise and superior results, Computer Aided Detection (CADe) system has been designed so that the radiologist can diagnose carcinoma in the ultrasonic images conveniently. CADe analyses the quality of the images, selects the region of interest, preprocesses the data, extracts the features and classifies the cancer.

Results: After exhaustive literature survey, Laws’ mask features and SVM classifier with Gaussian RBF kernels have been used in this paper. The experimentation was performed on 92 images using 50% - 50% training and testing criteria.

Conclusion: Comparative study reveals that our system for separating three class lung cancer provides 95.65% average accuracy for Laws' mask 3 dimensions using the SVM classifier that is maximum among the existing methods reported in the literature using the same dataset.

Keywords: Lung carcinoma, Laws' mask feature extraction, ultrasonic images, computer-aided diagnosis, non-small cell, SVM.

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