Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge
This work addresses skin lesion classification for medical diagnosis, but it appears incremental as it applies existing methods (VGG-NET and Random Forests) to a specific competition dataset without claiming major innovations.
The authors tackled the ISIC 2017 skin lesion classification challenge by developing an algorithm that uses VGG-NET and Random Forests for binary classification tasks involving melanoma, nevus, and seborrheic keratosis, but no concrete results or numbers are reported in the abstract.
This manuscript briefly describes an algorithm developed for the ISIC 2017 Skin Lesion Classification Competition. In this task, participants are asked to complete two independent binary image classification tasks that involve three unique diagnoses of skin lesions (melanoma, nevus, and seborrheic keratosis). In the first binary classification task, participants are asked to distinguish between (a) melanoma and (b) nevus and seborrheic keratosis. In the second binary classification task, participants are asked to distinguish between (a) seborrheic keratosis and (b) nevus and melanoma. The other phases of the competition are not considered. Our proposed algorithm consists of three steps: preprocessing, classification using VGG-NET and Random Forests, and calculation of a final score.