Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images

Skin cancer is a common form of cancer, and early detection increases the survival rate. To build deep learning models to classify dermal cell images and detect skin cancer. A model-driven architecture in the cloud, that uses deep learning algorithms in its core implementations, is used to construct...

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Veröffentlicht in:Informatics in medicine unlocked Jg. 18; S. 100282
Hauptverfasser: Kadampur, Mohammad Ali, Al Riyaee, Sulaiman
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 2020
Elsevier
Schlagworte:
Cancer
CNN
Deep cognition studio
Deep learning
Image classification
Model driven architecture
Deep learning
CNN
AI
Model driven architecture
Deep cognition studio
Cancer
Image classification
ISSN:2352-9148, 2352-9148
Online-Zugang:Volltext
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Zusammenfassung:Skin cancer is a common form of cancer, and early detection increases the survival rate. To build deep learning models to classify dermal cell images and detect skin cancer. A model-driven architecture in the cloud, that uses deep learning algorithms in its core implementations, is used to construct models that assist in predicting skin cancer with improved accuracy. The study illustrates the method of building models and applying them to classify dermal cell images. The deep learning models built here are tested on standard datasets, and the metric area under the curve of 99.77% was observed. A practitioner can use the model-driven architecture and quickly build the deep learning models to predict skin cancer.
ISSN:2352-9148
2352-9148
DOI:10.1016/j.imu.2019.100282