Machine Learning Approach for Skin Melanoma Classification

Melanoma is the most fatal type of malignant skin cancer, posing a serious threat to people's physical health. If melanoma of the skin is detected early, the chances of survival are very high. Dermoscopic images can be used to make an early diagnosis. Dermoscopy magnifies the skin, allowing dermatologists to better evaluate morphological features that are not easy to see with the naked eye. Machine learning is now an important technique for detecting various types of skin cancers. Convolutional Neural Network (CNN) has the potential to greatly assist dermatologists in the diagnosis of melanoma accurately. This paper uses a machine learning based approach to detect benign and malignant forms of skin cancer in dermoscopic images. The EfficientNet CNN model used here can design the appropriate network architecture to extract features with greater better accuracy and efficiency. The model is evaluated using dataset from the ISIC Archive.