Araguaia Medical Vision Lab at ISIC 2017 Skin Lesion Classification Challenge
This work addresses skin cancer diagnosis for medical imaging, but it is incremental as it applies existing methods to a specific challenge dataset.
The paper tackled the classification of skin lesion images for melanoma and seborrheic keratosis using deep convolutional neural networks, achieving AUC scores of 0.950 for seborrheic keratosis and 0.846 for melanoma.
This paper describes the participation of Araguaia Medical Vision Lab at the International Skin Imaging Collaboration 2017 Skin Lesion Challenge. We describe the use of deep convolutional neural networks in attempt to classify images of Melanoma and Seborrheic Keratosis lesions. With use of finetuned GoogleNet and AlexNet we attained results of 0.950 and 0.846 AUC on Seborrheic Keratosis and Melanoma respectively.