SOA-GF: Spiral Optimized Algorithm with Gabor Filter for Automatic Lung Cancer detection and Classification

  • D. Sathyanarayanan, Dr. S. Vatchala, R. Sridevi, A. Sathish
Keywords: Medical Image Processing, Deep Learning, Segmentation, Spiral Optimization Algorithm, Gabor Filter

Abstract

Lung cancer really has no earlier symptoms other than detecting some minute features pulmonary modules of a CT input image of a patient. The World Health Organization (WHO) report is not a good sign for doctors, radiologists, patients and the entire mankind. Computer Aided Detection (CAD) systems play a vital role in detecting these nodules and also help in minimizing the human error while processing a CT image. Many deep learning algorithms have been proposed in the recent past and produced significant results in bio-medical image processing. In this paper, we propose a Spiral Optimization Algorithm along with Gabor filter (SOA-GF) to perform feature extraction on the undertaken CT input group images. The classification was performed with Naïve Bayes, Random Tree, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). The experimentation results have shown that the algorithm performs well will all the classification techniques of this study, yet, its combination with CNN delivered better accuracy of 87.67% compared to other classification methods. The results were presented in both tabular values and graphical representation.

Published
2021-08-24
How to Cite
R. Sridevi, A. Sathish, D. S. D. S. V. (2021). SOA-GF: Spiral Optimized Algorithm with Gabor Filter for Automatic Lung Cancer detection and Classification. Design Engineering, 3464-3476. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/3698
Section
Articles