858P Automated detection of melanoma: Comparing a convolutional neural network (CNN) approach with an algorithm assessing disorder in the pattern of pigmented lesions, intended to mimick onco-dermatologists' visual analysis

Abstract

Among Computer Aided Diagnosis (CAD) systems for melanoma detection, CNN behaves as a “black box”, using a huge number of unknown features from a lesion’s image to come to a classification and where no expert input can be implemented. Conversely of hand-crafted methods, the expert is selecting features to be used for the lesions' classification. Most of these hand-crafted algorithms were based on ABCD criteria (asymmetry, border, color, diameter). However the unconscious process leading onco-dermatologists to arbitrate between nevi and melanoma is more likely based on a global cognitive assessment, which we hypothezise to be an overall perception of order/disorder in the pattern of pigmented lesions. Our objective was to design an hand-crafted model based on the concept of ordered/disordered pattern, and to assess its performances by comparison to a CNN model, for the classification melanoma versus nevus. We trained and tested 2 algorithms on a dermoscopic images dataset of 1533 melanomas and 6124 nevi. First, we developped The "Disorder model" based on a hand-crafted method, we extracted 4 specific features to characterize melanoma disorder named entropy, skewness, standard deviation and kurtosis, from 4 colors spaces. The classification of melanoma versus nevi was based on a statistical clustering method (KNN algorithm). Second, we developped a CNN model on the same dataset. Performances of the CNN model yielded: AUC 0.89, sensitivity 86%, specificity 75%, balanced accuracy 81%. The “Disorder model” reached similar high performances: AUC 0.91, sensitivity 91%, specificity 74%, balanced accuracy 82.5%. A statistical χ2 test revealed that entropy, characterizing color disorder, was the most discriminative feature for melanoma detection. This study shows that an algorithm attempting to mimick human brain ability, using a few features to assess the disorder pattern in pigmented lesions, can reach a level of performance for melanoma detection equivalent to CNN-based algorithm, which is using much more features. Entropy seems to be a key feature to assess pattern disorder of pigmented lesions.