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

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.

authors

Jain S

doi

10.2174/1573409916666200102122021

subject

Has Abstract

pub_date

2020-01-01 00:00:00

pages

833-840

issue

6

eissn

1573-4099

issn

1875-6697

pii

CAD-EPUB-103416

journal_volume

16

pub_type

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