Skin Cancer Classification using Random Forest
S. Nandhini1, Mohammed Abdul Sofiyan2, Sushant Kumar3, Adnan Afridi4
1MS. S. Nandhini*, Assistant Professor (O.G) at SRM Institute of science and technology.
2Mohammed Abdul Sofiyan, Department of B.tech in SRM Institute of science and technology.
3Sushant Kumar, Department of B.tech in SRM Institute of science and technology.
3Adnan Afridi, Department of B.tech in SRM Institute of science and technology.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 15, 2019. | Manuscript published on November 15, 2019. | PP: 39-42 | Volume-4 Issue-3, November 2019. | Retrieval Number: C0434114319/2019©BEIESP | DOI: 10.35940/ijmh.C0434.114319
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© The Authors. Published By: 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: Skin cancer is a very big health issue in today’s fast-growing population not only for old age people but for all age groups. We are classifying skin cancer of a person according to dermatoscopic images into seven different types. We handle this issue utilizing the HAM10000 (Human-Against-Machine with 10000 training images) data-set. The finalized dataset includes 10001 dermatoscopic pictures which are released as a readiness set for academic machine learning purposes and are openly available through the ISIC archive. We are classifying skin cancer of a person according to dermatoscopic images into seven different types.Through this research a person will get to know that if he/she suffering from any kind of skin cancer or not, so before going to consult any doctor a person will have some assurance about skin cancer.
Keywords: skin cancer, dermatoscopic, HAM10000, ISIC archive