BACKGROUND
In recent months, multiple publications have demonstrated the use of convolutional neural networks (CNN) to classify images of skin cancer as precisely as dermatologists. These CNNs failed to outperform the International Symposium on Biomedical Imaging (ISBI) 2016 challenge in terms of average precision, however, so the technical progress represented by these studies is limited. In addition, the available reports are difficult to reproduce, due to incomplete descriptions of training procedures and the use of proprietary image databases. These factors prevent the comparison of various CNN classifiers in equal terms.
OBJECTIVE
To demonstrate the training of an image-classifier CNN that outperforms the winner of the ISBI 2016 challenge by using open source images exclusively.
METHODS
A detailed description of the training procedure is reported while the used images and test sets are disclosed fully, to insure the reproducibility of our work.
RESULTS
Our CNN classifier outperforms all recent attempts to classify the original ISBI 2016 challenge test data (full set of 379 test images), with an average precision of 0.709 (vs. 0.637 of the ISBI winner) and with an area under the receiver operating curve of 0.85.
CONCLUSIONS
This work illustrates the potential for improving skin cancer classification with enhanced training procedures for CNNs, while avoiding the use of costly equipment or proprietary image data.
Multiclass Skin Cancer Classification using EfficientNets – A First Step towards Preventing Skin Cancer
