Article,
Skin Cancer Recognition Using SVM Image Processing Technique
BOHR International Journal of Biocomputing and Nano Technology, 1 (1): 11-15 (April 2020)
DOI: https://doi.org/10.54646/bijbnt.004
Abstract
Skin cancer is considered as commonest cause of death among humans in today’s world. This type of cancer shows non uniform or patchy growth of skin cells that most commonly occurs on of the certain parts of body which are more likely exposed to the light, but it can occur anywhere on the body. The majority of skin cancers can be treated if detected early. As a result, finding skin cancer early and easily will save a patient’s life. Early detection of skin cancer at an early stage is now possible thanks to modern technologies. Biopsy procedure 1 is a systematic method for diagnosis skin cancer. It is achieved by extracting skin cells, after which the sample is sent to different laboratories for examination. It’s a very long (in terms of time) and painful process. For primitive detection of skin cancer disease, we proposed a skin cancer detection system based on svm. It is more helpful to patients. Various methods of image processing and the supervised learning algorithm called Support Vector Machime (SVM) are used in the identification process. Epiluminescence microscopy is taken using an image and particular to several pre processing techniques which are used in the reduction of sound artifacts and improvise quality of images. Segmentation is done by using certain thresholding techniques like OTSU. The GLCM technique must be used to remove certain image features. These characteristics are fed into the classifier as input. The Supervised learning model called (SVM) is used to distinguish data sets. It determines whether a picture is cancerous or not
