Abstract 858P
Background
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.
Methods
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.
Results
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.
Conclusions
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.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
J.J. Grob and D. Merad.
Funding
Aderem group.
Disclosure
J. Monnier: Financial Interests, Institutional, Invited Speaker: BMS. C. Gaudy Marqueste: Financial Interests, Institutional, Invited Speaker: BMS, Roche. J.J. Grob: Financial Interests, Institutional, Invited Speaker: BMS, Amgen, MSD, Novartis, Roche, Pierre Fabre, Merck, Pfizer. All other authors have declared no conflicts of interest.