Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2019
Data augmentation in dermatology image recognition using machine learning.
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Aggarwal St Lt Pushkar
Аннотация
BACKGROUND: Each year in the United States, over 80 million people are affected by acne, atopic dermatitis, rosacea, psoriasis, and impetigo. Artificial intelligence and machine learning could prove to be a good tool for assisting in the diagnosis of dermatological conditions. The objective of this study was to evaluate the use of data augmentation in machine learning image recognition of five dermatological disease manifestations-acne, atopic dermatitis, impetigo, psoriasis, and rosacea.
MATERIALS AND METHODS: Open-source dermatological images were gathered and used to retrain TensorFlow Inception version-3. Retraining was done twice-once with and once without data augmentation. Both models were tested with the same images, and R software was used to perform statistical analysis.
RESULTS: The average of each of the statistical measures (sensitivity, specificity, PPV, NPN, MCC, and F1 Score) increased when data augmentation was added to the model. In particular, the average Matthews correlation coefficient increased by 7.7%. Each of the five dermatological manifestations had an increase in area under the curve (AUC) after data augmentation with the average increase in AUC of 0.132 and a standard deviation of 0.033. Atopic dermatitis had the highest increase in AUC of 0.18. With data augmentation, the lowest AUC was 0.87 for psoriasis and the highest was 0.97 for acne, indicating that the model performs well.
CONCLUSION: With a deep learning-based approach, it is possible to differentiate dermatological images with appreciable MCC, F1 score, and AUC. Further, data augmentation can be used to increase the model's accuracy by a significant amount.
MATERIALS AND METHODS: Open-source dermatological images were gathered and used to retrain TensorFlow Inception version-3. Retraining was done twice-once with and once without data augmentation. Both models were tested with the same images, and R software was used to perform statistical analysis.
RESULTS: The average of each of the statistical measures (sensitivity, specificity, PPV, NPN, MCC, and F1 Score) increased when data augmentation was added to the model. In particular, the average Matthews correlation coefficient increased by 7.7%. Each of the five dermatological manifestations had an increase in area under the curve (AUC) after data augmentation with the average increase in AUC of 0.132 and a standard deviation of 0.033. Atopic dermatitis had the highest increase in AUC of 0.18. With data augmentation, the lowest AUC was 0.87 for psoriasis and the highest was 0.97 for acne, indicating that the model performs well.
CONCLUSION: With a deep learning-based approach, it is possible to differentiate dermatological images with appreciable MCC, F1 score, and AUC. Further, data augmentation can be used to increase the model's accuracy by a significant amount.