Highlights
- •Auto intelligences (AI) trained by conventional photo images show less performance.
- •We trained DeepLabv3 + -based CNN segmentation model segmenting skin and lesion area.
- •The CNN segmentation model can generate image datasets suitable for AI diagnosis.
- •AI diagnosis in the CNN-segmented images was better than the original images.
- •Our CNN segmentation model will improve AI classification in skin disease images.
Abstract
Background
For dermatological practices, non-standardized conventional photo images are taken
and collected as a mixture of variable fields of the image view, including close-up
images focusing on designated lesions and long-shot images including normal skin and
background of the body surface. Computer-aided detection/diagnosis (CAD) models trained
using non-standardized conventional photo images exhibit lower performance rates than
CAD models that detect lesions in a localized small area, such as dermoscopic images.
Objective
We aimed to develop a convolutional neural network (CNN) model for skin image segmentation
to generate a skin disease image dataset suitable for CAD of multiple skin disease
classification.
Methods
We trained a DeepLabv3 + -based CNN segmentation model to detect skin and lesion areas
and segmented out areas that satisfy the following conditions: more than 80% of the
image will be the skin area, and more than 10% of the image will be the lesion area.
Results
The generated CNN-segmented image database was examined using CAD of skin disease
classification and achieved approximately 90% sensitivity and specificity to differentiate
atopic dermatitis from malignant diseases and complications, such as mycosis fungoides,
impetigo, and herpesvirus infection. The accuracy of skin disease classification in
the CNN-segmented image dataset was almost equal to that of the manually cropped image
dataset and higher than that of the original image dataset.
Conclusion
Our CNN segmentation model, which automatically extracts lesions and segmented images
of the skin area regardless of image fields, will reduce the burden of physician annotation
and improve CAD performance.
Abbreviations:
CAD (computer-aided diagnosis), AD (atopic dermatitis), CNN (convolutional neural network), NSDD (National Skin Disease Database), AI (artificial intelligence), MF (mycosis fungoides), IM (impetigo), HI (herpesvirus infection), AUC (area under the curve)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: January 10, 2023
Accepted:
January 10,
2023
Received in revised form:
December 7,
2022
Received:
March 16,
2022
Publication stage
In Press Journal Pre-ProofIdentification
Copyright
© 2023 Japanese Society for Investigative Dermatology. Published by Elsevier B.V. All rights reserved.
