Hybrid Feature Extraction and Firefly Based Feature Selection Technique for Lung Cancer Computer Aided Diagnosis

  • B.Mohamed fazze basha, Dr.M.Mohamed surputheen

Abstract

            Lung malignant progress is one of the hazardous and life taking sickness on the globe. Be that as it may, early analysis and treatment can spare life. In spite of the statement that, CT output imaging is best imaging procedure in restorative field, it is hard for specialists to decipher and distinguish the disease from CT sweep representations. In this method PC supported conclusion can be useful for specialists to recognize the destructive cells precisely. Numerous PC helped systems consuming image preparing and AI has been looked into and executed. The fundamental point of this exploration is to assess the different PC helped procedures, examining the present best method and discovering their impediment and disadvantages lastly proposing the new model with upgrades in the present best model the new proposed model has consist a KFCM based hybrid segmentation, Hybrid feature extraction method and firefly constructed feature selection system. In the Hybrid feature extraction technique is a combination of Discrete Wavelet Transform (DWT) and Gray-Level Co-occurrence Matrix (GLCM). This techniques improves the projected structure presentation in terms of accuracy. To analysis the proposed prediction and classification the future prototypical has associated with the various existing technique. The qualitative and measureable analysis shows that the proposed model provides best results compared to other models.

Published
2019-11-06
How to Cite
Dr.M.Mohamed surputheen, B. fazze basha,. (2019). Hybrid Feature Extraction and Firefly Based Feature Selection Technique for Lung Cancer Computer Aided Diagnosis. International Journal of Advanced Science and Technology, 28(13), 587 - 596. Retrieved from http://sersc.org/journals/index.php/IJAST/article/view/1367
Section
Articles