• Keerthan N
• Keerthi S
• Likhit S
• M Samyama

Skin cancer is a widespread, global, and potentially deadly disease, which over the last three decades has afflicted more lives in the USA than all other forms of cancer combined. There have been a lot of promising recent works utilizing deep network architectures for developing automated skin lesion segmentation. Melanin is the pigment that discerns the color of human skin. The special cells produce melanin in the skin. If these cells are damaged or unhealthy, skin discoloration is visible. Skin pigment discoloration is a hazardous fact as a symptom of human skin disease with a possibility of losing natural beauty. The extracted information of the skin discoloration can work as a guide to diagnosis the disease. In this project, different imaging techniques like watershed method, edge detection and morphological operations are used to analyze and extract the information of discoloration lesion by measuring the pixel number of lesions on skin. In this paper, an image processing method that has been initially developed for plant disease recognition is appropriately adapted for the detection of skin disorders. The image analyzing results are visually examined by the skin specialist and are observed to be highly accurate. The visual results are presented in the description and the accuracy of mathematical analysis is 94.88 percent. This system will give more accuracy and will generate results faster than the traditional method, making this application an efficient and dependable system for dermatological disease detection. Furthermore, this can also be used as a reliable real time teaching tool for medical students in the dermatology stream.

— Skin cancer is a widespread, global, and potentially deadly disease, which over the last three decades has afflicted more lives in the USA than all other forms of cancer combined. There have been a lot of promising recent works utilizing deep network architectures for developing automated skin lesion segmentation. Melanin is the pigment that discerns the color of human skin. The special cells produce melanin in the skin. If these cells are damaged or unhealthy, skin discoloration is visible. Skin pigment discoloration is a hazardous fact as a symptom of human skin disease with a possibility of losing natural beauty. The extracted information of the skin discoloration can work as a guide to diagnosis the disease. In this project, different imaging techniques like watershed method, edge detection and morphological operations are used to analyze and extract the information of discoloration lesion by measuring the pixel number of lesions on skin. In this paper, an image processing method that has been initially developed for plant disease recognition is appropriately adapted for the detection of skin disorders. The image analyzing results are visually examined by the skin specialist and are observed to be highly accurate. The visual results are presented in the description and the accuracy of mathematical analysis is 94.88 percent. This system will give more accuracy and will generate results faster than the traditional method, making this application an efficient and dependable system for dermatological disease detection. Furthermore, this can also be used as a reliable real time teaching tool for medical students in the dermatology stream.

"Skin Cancer Detection Using Image Processing ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.1545-1548, June 2020, Available :http://www.jetir.org/papers/JETIR2006556.pdf

"Skin Cancer Detection Using Image Processing ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp1545-1548, June 2020, Available at : http://www.jetir.org/papers/JETIR2006556.pdf