Абстрактный

Malignant melanoma classification using machine learning algorithms.

Rashmi Patil, Sreepathi Bellary

Nowadays growth of malignant skin causes the death of patients and is a highly identified reason for death among people. The unusual growth of skin cells on any part of the body happens, and it comes to exposure of sunlight is known as malignant skin. If malignant skin cancer is detected at an early stage, then a huge portion of skin malignant growth is recoverable so it can save the patient's life. The identification of melanoma skin cancer is possible at an early stage by using novel methods. Here we described two schemes for detecting melanoma from benign dermoscopic pictures. The first scheme combines the Convolutional Neural Network (CNN) with the Support Vector Machine (SVM), that is, CNN+SVM and the second scheme combines CNN with Extreme Gradient Boosting (XGB), that is, CNN+XGB, because CNN works well for the training dataset, and the SVM work fine in classification also XGB used as a recognizer. The total of 2000 images are taken and achieved accuracy 84.0% for CNN+SVM and for 88.0% CNN+XGB