Apprentissage profond pour l’aide au diagnostic du mélanome à partir d’exemple
Abstract
One study reveals that 15404 new cases of cutaneous melanoma have been estimated in France in 2017. The 5-year survival rate of a person with advanced melanoma is much lower than 20%, which raises the need for diagnose it at an early stage. The purpose of this work is to build a supervised computer-aided diagnosis system for melanoma. The database used for the implementation includes 1356 images divided into 9 classes. Two approaches have been implemented : classical approach and deep learning approach. The classical approach combines
two support vector machine classifiers (SVM) trained on features extracted from three extractors. This approach yielded an area under the receptor curve (AUC) of 0.88, a sensitivity (SE) of 89% and a specificity (SPEC) of 77%. The deep learning approach uses features extracted from two pre-trained models VGG16 and resnet50 to train two linear SVM. The scores from these two classifiers are combined using a logistic regression algorithm to obtain the classification. This approach yielded an AED-CCR of 0.88, SE of 78% and SPEC of 83%.
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