JMIR Publications

ABSTRACT

Background:

The lack of dark skin images in pathologic skin lesions in dermatology resources hinders the accurate diagnosis of skin lesions in people of color. Artificial intelligence applications have further disadvantaged people of color because those applications are mainly trained with light-skinned images.

Objective:

The aim of this study is to develop a deep learning system that generates realistic images of darker skin colors to improve dermatology data diversity for various malignant and benign lesions.

Methods:

We collected skin clinical images for common malignant and benign skin conditions from DermNet NZ, the International Skin Imaging Collaboration, and Dermatology Atlas. Two deep learning methods, style transfer (ST) and deep blending (DB) were utilized to generate images with darker skin tones using the lighter skin images. The generated images were evaluated quantitively, and qualitatively. Furthermore, a convolutional neural network (CNN) was trained using the generated images to assess the latter’s effect on skin lesion classification accuracy.

Results:

Image quality assessment showed that the ST method outperformed DB as the former achieved a lower loss of realism score of 0·23 [95% CI 0·19–0·27] compared to 0·63 [95% CI 0·59–0·67] for the DB method. In addition, ST achieved a higher disease presentation with a similarity score of 0.44 [95% CI 0.40–0.49] compared to 0.17 [95% CI 0.14–0.21] for the DB method. The qualitative assessment completed on masked participants indicated that ST-generated images exhibited high realism where 62.0% of the generated images were classified as real. Eight dermatologists correctly diagnosed the lesions in the generated images with an average rate of 0.75 for several malignant and benign lesions. Finally, the classification accuracy and the area under the curve (AUC) of the model when considering the generated images were 0.76 [95% CI 0.73-0.79] and 0.72 [95% CI 0.68-76], respectively, compared to the accuracy of 0.56 [95% CI 0.52-0.60] and AUC of 0.66 [95% CI 0.63-0.70] for the model without considering the generated images.

Conclusions:

Deep learning approaches can generate realistic skin lesion images that improve the skin tone diversity of dermatology atlases. The diversified image bank, utilized herein to train a CNN, demonstrates the potential of developing generalizable artificial intelligence skin cancer diagnosis applications.