Classification of Malignant Melanoma and Benign Lung Cancer by using Deep Learning Based Neural Network
S. Santhi1, R. Adaline Suji2, E. Udayakumar3
1S. Santhi*, Professor, Department of Electronics and Communication Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamil Nadu, India.
2R. Adaline Suji, Associate Professor, Department of Computer Science and Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamil Nadu, India.
3E. Udayakumar, Assistant Professor, Department of Electronics and Communication Engineering, KIT-Kalaignarkarunanidhi Institute of Technology, Coimbatore, Tamil Nadu, India.
Manuscript received on December 15, 2019. | Revised Manuscript received on December 20, 2019. | Manuscript published on January 10, 2020. | PP: 2958-2963 | Volume-9 Issue-3, January 2020. | Retrieval Number: K17040981119/2020©BEIESP | DOI: 10.35940/ijitee.K1704.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Humanoid Tumor is one of the utmost hazardous syndromes which is mostly affected by heritable uncertainty of manifold molecular modifications. Midst numerous methods of humanoid tumor, Lung cancer is the utmost communal one. To classify Lung cancer at an initial phase and examine them over several procedures entitled as segmentation and feature extraction. Here, in this scheme is suggested to emphasis extraordinary attentiveness of Melanoma Heir which bases the Lung Cancer. This development is based on samples replica skill is used for malignant melanoma Lung tumor recognition. In this scheme dissimilar stage for melanoma Lung cancer lesion classification i.e., first the Image Gaining Method, preprocessing, separation, define piece for Lung cancer Feature Collection regulates lesion description, classification methods. In the Feature abstraction by numerical image treating method includes, regularity detection, Border Detection, color, and width discovery and also we used GLCM for excerpt the surface based features. Here we planned the Neural Network to categorize the benign or malignant stage.
Keywords: Water shield Transform Procedures, Gray-Level Co-Occurrence Matrix (GLCM), Probabilistic Neural Network (PNN).
Scope of the Article: Classification