Automated grading of acne vulgaris by deep learning with convolutional neural networks

Automated grading of acne vulgaris by deep learning with convolutional neural networks
Title:
Automated grading of acne vulgaris by deep learning with convolutional neural networks
Other Titles:
Skin Research & Technology
DOI:
10.1111/srt.12794
Publication Date:
29 September 2019
Citation:
Lim, ZV, Akram, F, Ngo, CP, et al. Automated grading of acne vulgaris by deep learning with convolutional neural networks. Skin Res Technol. 2020; 26: 187– 192. https://doi.org/10.1111/srt.12794
Abstract:
BACKGROUND: The visual assessment and severity grading of acne vulgaris by physicians can be subjective, resulting in inter- and intra-observer variability. OBJECTIVE: To develop and validate an algorithm for the automated calculation of the Investigator's Global Assessment (IGA) scale, to standardize acne severity and outcome measurements. MATERIALS AND METHODS: A total of 472 photographs (retrieved 01/01/2004-04/08/2017) in the frontal view from 416 acne patients were used for training and testing. Photographs were labeled according to the IGA scale in three groups of IGA clear/almost clear (0-1), IGA mild (2), and IGA moderate to severe (3-4). The classification model used a convolutional neural network, and models were separately trained on three image sizes. The photographs were then subjected to analysis by the algorithm, and the generated automated IGA scores were compared to clinical scoring. The prediction accuracy of each IGA grade label and the agreement (Pearson correlation) of the two scores were computed. RESULTS: The best classification accuracy was 67%. Pearson correlation between machine-predicted score and human labels (clinical scoring and researcher scoring) for each model and various image input sizes was 0.77. Correlation of predictions with clinical scores was highest when using Inception v4 on the largest image size of 1200 × 1600. Two sets of human labels showed a high correlation of 0.77, verifying the repeatability of the ground truth labels. Confusion matrices show that the models performed sub-optimally on the IGA 2 label. CONCLUSION: Deep learning techniques harnessing high-resolution images and large datasets will continue to improve, demonstrating growing potential for automated clinical image analysis and grading.
License type:
PublisherCopyrights
Funding Info:
There was no specific funding for research done.
Description:
This is the peer reviewed version of the following article: Lim, ZV, Akram, F, Ngo, CP, et al. Automated grading of acne vulgaris by deep learning with convolutional neural networks. Skin Res Technol. 2020; 26: 187– 192. https://doi.org/10.1111/srt.12794, which has been published in final form at https://doi.org/10.1111/srt.12794. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
ISSN:
0909-752X
1600-0846
Files uploaded:

File Size Format Action
1118a-automated-grading-of-acne-vulgaris.pdf 2.22 MB PDF Open