The Possibility of Selective Skin Lesion Classification in Convolutional Neural Networks

  • Evelyn Anyebe Postgraduate student University of Dundee,Dundee, DD1 4HN, United Kingdom
Keywords: skin lesion classification, selective classification, deep learning, uncertainty estimation, convolutional neural network(CNNs), MC dropout


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.


. Esteva et al, “Dermatologist-level classification of skin cancer with deep nueral networks,” Nature, vol. 542, pp. 115-217, 2017.

. A. Mobiny, A. Singh, and H.V. Nguyen, “Risk-Aware Machine Learning Classifier for skin lesion diagnosis,” Journal of clinical medicine, vol. 8, no. 8, p. 1241, 2019.

. Y. Fujisawa, S. Inuoe, and Y. Nakamura, “The possibility of deep learning based computer-aided skin tumour classifier,” Frontiers in medicine, vol. 6, pp. 1-10, 2019.

. S. Chan et al, “Machine Learning in Dermatology: Current Applications, opportunities and limitations,” Adis Journals, 2020.

. N. Gessert et al, “Skin Lesion Classification Using Loss Balancing and ensemble of Multi-Resolution EfficientNets,” in ISIC, 2019.

. Y. Geifman, and R. El-Yaniv, “Selectivenet: a deep neural network with and integrated reject,” 2019.

. C. Cortes, G. DeSalvo, and M. Mohri, “Boosting with abstention,” In Advances in Neural, pp. 1660-1668, 2016.

. K. Murphy, in Machine learning: A probabilistic perspective, MIT Press, 2012.

. Y. Kwon et al, “Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation”.

. Y. Gal, “Uncertainty in deep learning,” University of Cambridge, Cambridge, 2016.

. P.V Molle et al, “Quantifying Uncertainty of Deep Neural Networks in skin lesion classification,” 2019.

. ISIC, “Challenge2019.Isic-Archive,” ISIC. (n.d.), 2019. [Online]. Available: [Accessed 2020 05 2020].

. Z. Apalla et al, “Skin cancer: Epidemiology, disease burden, pathophysiology, diagnosis, and therapeutic approches,” Demartol. Ther, pp. 7,5-19, 2017.

. C. R. UK, “Melanoma skin cancer incidence statistics,” 27 06 2020. [Online]. Available:

. N. C. Institute, “Surveilance, Epidemology and end results program: Cancer stats,” 25 06 2020. [Online]. Available:

. I. Goodfellow et al, Deep Learning, MIT Press, 2016.

. E. Hullermeier, and W. Waegeman,, “Aleatoric and Epistemic Uncertainty in Machine,” 2020. [Online]. Available: [Accessed 10 07 2020].

. Y. Gal and Ghahramani, “Bayesian convolutional neural networks with Bernoulli approximate variational inference,” 2016.

. Y. Geifman, and R. El-Yaniv, “Selective classification for deep neural networks,” In Advances in neural information processing systems, no. b, pp. 4878-4887, 2017.

. C. Cortes, G. DeSalvo, and M. Mohri, “Learning with rejection,” In International Conference on Algorithmic Learning Theory, pp. 62-82, 2016.

. K. Hamid, A. Asif, W. Abbasi, D. Sabih, and F. U. A. A. Minhas, “Machine Learning with Abstention for Automated Liver Disease Diagnosis,” International Conference on Frontiers of Information Technology (FIT), Vols. 356-361, 2017.

. M. Tan, and V. Q. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in ICML, California, 2019.