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.