Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm

We tested the use of a deep learning algorithm to classify the clinical images of 12 skin diseases-basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, actinic keratosis, seborrheic keratosis, malignant melanoma, melanocytic nevus, lentigo, pyogenic granuloma, hemangioma, dermat...

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Vydané v:	Journal of investigative dermatology Ročník 138; číslo 7; s. 1529
Hlavní autori:	Han, Seung Seog, Kim, Myoung Shin, Lim, Woohyung, Park, Gyeong Hun, Park, Ilwoo, Chang, Sung Eun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States 01.07.2018
Predmet:	Adult Aged Aged, 80 and over Area Under Curve Biopsy Datasets as Topic Deep Learning Diagnosis, Differential False Positive Reactions Female Granuloma, Pyogenic - diagnostic imaging Granuloma, Pyogenic - pathology Humans Image Processing, Computer-Assisted - methods Keratosis, Actinic - diagnostic imaging Keratosis, Actinic - pathology Keratosis, Seborrheic - diagnostic imaging Keratosis, Seborrheic - pathology Lentigo - diagnostic imaging Lentigo - pathology Male Middle Aged Photography Predictive Value of Tests ROC Curve Skin - diagnostic imaging Skin - pathology Skin Neoplasms - diagnostic imaging Skin Neoplasms - pathology Software Warts - diagnostic imaging Warts - pathology Young Adult
ISSN:	1523-1747, 1523-1747
On-line prístup:	Zistit podrobnosti o prístupe
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Abstract	We tested the use of a deep learning algorithm to classify the clinical images of 12 skin diseases-basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, actinic keratosis, seborrheic keratosis, malignant melanoma, melanocytic nevus, lentigo, pyogenic granuloma, hemangioma, dermatofibroma, and wart. The convolutional neural network (Microsoft ResNet-152 model; Microsoft Research Asia, Beijing, China) was fine-tuned with images from the training portion of the Asan dataset, MED-NODE dataset, and atlas site images (19,398 images in total). The trained model was validated with the testing portion of the Asan, Hallym and Edinburgh datasets. With the Asan dataset, the area under the curve for the diagnosis of basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, and melanoma was 0.96 ± 0.01, 0.83 ± 0.01, 0.82 ± 0.02, and 0.96 ± 0.00, respectively. With the Edinburgh dataset, the area under the curve for the corresponding diseases was 0.90 ± 0.01, 0.91 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. With the Hallym dataset, the sensitivity for basal cell carcinoma diagnosis was 87.1% ± 6.0%. The tested algorithm performance with 480 Asan and Edinburgh images was comparable to that of 16 dermatologists. To improve the performance of convolutional neural network, additional images with a broader range of ages and ethnicities should be collected.
AbstractList	We tested the use of a deep learning algorithm to classify the clinical images of 12 skin diseases-basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, actinic keratosis, seborrheic keratosis, malignant melanoma, melanocytic nevus, lentigo, pyogenic granuloma, hemangioma, dermatofibroma, and wart. The convolutional neural network (Microsoft ResNet-152 model; Microsoft Research Asia, Beijing, China) was fine-tuned with images from the training portion of the Asan dataset, MED-NODE dataset, and atlas site images (19,398 images in total). The trained model was validated with the testing portion of the Asan, Hallym and Edinburgh datasets. With the Asan dataset, the area under the curve for the diagnosis of basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, and melanoma was 0.96 ± 0.01, 0.83 ± 0.01, 0.82 ± 0.02, and 0.96 ± 0.00, respectively. With the Edinburgh dataset, the area under the curve for the corresponding diseases was 0.90 ± 0.01, 0.91 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. With the Hallym dataset, the sensitivity for basal cell carcinoma diagnosis was 87.1% ± 6.0%. The tested algorithm performance with 480 Asan and Edinburgh images was comparable to that of 16 dermatologists. To improve the performance of convolutional neural network, additional images with a broader range of ages and ethnicities should be collected.We tested the use of a deep learning algorithm to classify the clinical images of 12 skin diseases-basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, actinic keratosis, seborrheic keratosis, malignant melanoma, melanocytic nevus, lentigo, pyogenic granuloma, hemangioma, dermatofibroma, and wart. The convolutional neural network (Microsoft ResNet-152 model; Microsoft Research Asia, Beijing, China) was fine-tuned with images from the training portion of the Asan dataset, MED-NODE dataset, and atlas site images (19,398 images in total). The trained model was validated with the testing portion of the Asan, Hallym and Edinburgh datasets. With the Asan dataset, the area under the curve for the diagnosis of basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, and melanoma was 0.96 ± 0.01, 0.83 ± 0.01, 0.82 ± 0.02, and 0.96 ± 0.00, respectively. With the Edinburgh dataset, the area under the curve for the corresponding diseases was 0.90 ± 0.01, 0.91 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. With the Hallym dataset, the sensitivity for basal cell carcinoma diagnosis was 87.1% ± 6.0%. The tested algorithm performance with 480 Asan and Edinburgh images was comparable to that of 16 dermatologists. To improve the performance of convolutional neural network, additional images with a broader range of ages and ethnicities should be collected. We tested the use of a deep learning algorithm to classify the clinical images of 12 skin diseases-basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, actinic keratosis, seborrheic keratosis, malignant melanoma, melanocytic nevus, lentigo, pyogenic granuloma, hemangioma, dermatofibroma, and wart. The convolutional neural network (Microsoft ResNet-152 model; Microsoft Research Asia, Beijing, China) was fine-tuned with images from the training portion of the Asan dataset, MED-NODE dataset, and atlas site images (19,398 images in total). The trained model was validated with the testing portion of the Asan, Hallym and Edinburgh datasets. With the Asan dataset, the area under the curve for the diagnosis of basal cell carcinoma, squamous cell carcinoma, intraepithelial carcinoma, and melanoma was 0.96 ± 0.01, 0.83 ± 0.01, 0.82 ± 0.02, and 0.96 ± 0.00, respectively. With the Edinburgh dataset, the area under the curve for the corresponding diseases was 0.90 ± 0.01, 0.91 ± 0.01, 0.83 ± 0.01, and 0.88 ± 0.01, respectively. With the Hallym dataset, the sensitivity for basal cell carcinoma diagnosis was 87.1% ± 6.0%. The tested algorithm performance with 480 Asan and Edinburgh images was comparable to that of 16 dermatologists. To improve the performance of convolutional neural network, additional images with a broader range of ages and ethnicities should be collected.
Author	Park, Ilwoo Han, Seung Seog Park, Gyeong Hun Lim, Woohyung Kim, Myoung Shin Chang, Sung Eun
Author_xml	– sequence: 1 givenname: Seung Seog surname: Han fullname: Han, Seung Seog organization: I Dermatology Clinic, Seoul, Korea – sequence: 2 givenname: Myoung Shin surname: Kim fullname: Kim, Myoung Shin organization: Department of Dermatology, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea – sequence: 3 givenname: Woohyung surname: Lim fullname: Lim, Woohyung organization: SK Telecom, Human Machine Interface Technology Laboratory, Seoul, Korea – sequence: 4 givenname: Gyeong Hun surname: Park fullname: Park, Gyeong Hun organization: Department of Dermatology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Dongtan, Korea – sequence: 5 givenname: Ilwoo surname: Park fullname: Park, Ilwoo organization: Department of Radiology, Chonnam National University Medical School and Hospital, Gwangju, Korea – sequence: 6 givenname: Sung Eun surname: Chang fullname: Chang, Sung Eun email: csesnumd@gmail.com organization: Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea. Electronic address: csesnumd@gmail.com
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/29428356$$D View this record in MEDLINE/PubMed
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PublicationTitle	Journal of investigative dermatology
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References	29864435 - J Invest Dermatol. 2018 Oct;138(10):2277-2279 29864434 - J Invest Dermatol. 2018 Oct;138(10):2275-2277
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Snippet	We tested the use of a deep learning algorithm to classify the clinical images of 12 skin diseases-basal cell carcinoma, squamous cell carcinoma,...
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SubjectTerms	Adult Aged Aged, 80 and over Area Under Curve Biopsy Datasets as Topic Deep Learning Diagnosis, Differential False Positive Reactions Female Granuloma, Pyogenic - diagnostic imaging Granuloma, Pyogenic - pathology Humans Image Processing, Computer-Assisted - methods Keratosis, Actinic - diagnostic imaging Keratosis, Actinic - pathology Keratosis, Seborrheic - diagnostic imaging Keratosis, Seborrheic - pathology Lentigo - diagnostic imaging Lentigo - pathology Male Middle Aged Photography Predictive Value of Tests ROC Curve Skin - diagnostic imaging Skin - pathology Skin Neoplasms - diagnostic imaging Skin Neoplasms - pathology Software Warts - diagnostic imaging Warts - pathology Young Adult
Title	Classification of the Clinical Images for Benign and Malignant Cutaneous Tumors Using a Deep Learning Algorithm
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