A Comparative Study for Classification of Skin Cancer

Skin cancer is one of the most common types of cancer all over the world. It is easily treatable when it is detected in its beginning stage. Melanoma is the most dangerous form of skin cancer. Early detection of melanoma is important in reducing the mortality rate of skin cancer. Recently, machine learning has become an efficient method in classifying skin lesions as melanoma or benign. Main features for this task include color, texture and shape. A comparative study about color, texture and shape features of melanoma is useful for future research of skin cancer classification. Inspired by this fact, our study compares the classification results of 6 classifiers in combination with 7 feature extraction methods and 4 data preprocessing steps on the two largest datasets of skin cancer. Our findings reveal that a system consisting of Linear Normalization of the input image as data preprocessing step, HSV as feature extraction method and Balanced Random Forest as classifier yields best prediction results on the HAM10000 dataset with 81.46% AUC, 74.75% accuracy, 90.09% sensitivity and 72.84 % specificity.

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