The Possibility of Selective Skin Lesion Classification in Convolutional Neural Networks
Keywords:
skin lesion classification, selective classification, deep learning, uncertainty estimation, convolutional neural network(CNNs), MC dropoutAbstract
Selective classification of skin lesion images and uncertainty estimation is examined to increase the adoption of convolutional neural networks(CNNs) in automated skin cancer diagnostic systems. Research on the application of deep learning models to skin cancer diagnosis has shown success as models outperform medical experts [1]. However, concerns on uncertainty in classifiers and difficulty in approximating uncertainty has caused limited adoption of CNNs in Computer-aided diagnostic systems (CADs) in health care. This research propose selective classification to increase confidence in CNN models for skin cancer diagnosis. The methodology is based on SoftMax response(SR), MC dropout and risk-coverage performance evaluation metric. Risk-coverage curves gives physicians and dermatologist information about the expected rate of misclassification by a model. This enable them to measure the reliability of the classifier’s predictions and inform their decision during skin cancer diagnosis. MC dropout uncertainty estimate was shown to increase accuracy for Melanoma detection by 1.48%. The proposed selective classifier achieved increase melanoma detection. The sensitivity of melanoma increased by 9.91% and 9.73% after selective classification at a coverage of 0.7. This study showed that selective classification and uncertainty estimation can be combined to promote adoption of CNNs in CADs for skin lesions classification.
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