Design and Implement of Deep Learning Model to Detect the Melanoma
Patange Srujeeth Kumar1, Deepak Sukheja2, G. Ramesh Chandra3
1Patange Srujeeth Kumar, Department of Computer Science Engineering, VNR Vignana Jyothi Institute of Engineering & Technology, India.
2Dr. Deepak Sukheja, Department of Computer Science Engineering, VNR Vignana Jyothi Institute of Engineering & Technology, India.
3Dr. G. Ramesh Chandra, Department of Computer Science Engineering, VNR Vignana Jyothi Institute of Engineering & Technology, India. 

Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3497-3504 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6611018520/2020©BEIESP | DOI: 10.35940/ijrte.E6611.018520

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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: Detecting Skin lesions on the human body is a big task to the doctors in the initial stage because of the low contrast on the body. This skin cancer can be occur due to sun rays. If the disease cannot detect in early stage, there it may cause death to human lives. Here there are some algorithms to predict the melanoma using deep learning techniques. ISIC International Skin Imaging Collaboration Archive set where it provides various images of melanoma and non-melanoma. There are so many challenges to identify the image with melanoma and non-melanoma types of skin cancer. In this paper we applied hair removal algorithm and k-means clustering algorithm where to remove unwanted substances from the original images. To classify the melanoma and non-melanoma skin cancer, this paper proposed prediction process and sequential CNN architecture.
Keywords: Melanoma, Skin Cancer, Deep Learning, Classification, CNN, Sequential model.
Scope of the Article: Deep Learning.