• Rutuja Tallapalli
• Harshada Mhaske
• Ajaykumar Shendage
• Mandar Patil
• Jeevan Thote

The skin cancer melanoma is very dangerous. Because it is difficult for dermatologists to analyze each patient sample carefully, a decision support system is required to assess the risk involved with a particular sample. Skin cancer is one of the fatal diseases that people suffer from today. Skin cancer is primarily brought on by the abnormal proliferation of melanocytic cells. Due to genetic factors and UV exposure, melanoma appears on the skin as a brown or black color. With early detection, this skin cancer can be completely cured. The traditional, invasive, and painful method of finding skin cancer is biopsy. This type of laboratory testing takes longer. Computer-aided skin cancer diagnosis has been developed to address the problems. To find skin cancer, the proposed system employs four phases. Dermoscopy is first used to take a picture of the skin. The image must then be pre-processed. Following the pre-processing stage, the lesion is segmented, and then specific features from the segmented lesion are extracted. Finally, various classifiers was given these features to determine whether the given image was of normal skin or one with melanoma disease.

Melanoma Detection, Image Segmentation, Melanoma Skin Cancer, Feature Extraction, CNN, SVM Classifier, Logistic Regression, Decision Trees, Gradient Boosting, Bagging Classifier, XGBoost Classifier.

"Melanoma Cancer Detection Using Artificial Intelligence", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.e596-e599, April-2024, Available :http://www.jetir.org/papers/JETIR2404464.pdf

"Melanoma Cancer Detection Using Artificial Intelligence", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppe596-e599, April-2024, Available at : http://www.jetir.org/papers/JETIR2404464.pdf