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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 2/2018

Open Access 01-07-2018 | Research

Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge

Authors: Xinyuan Zhang, Shiqi Wang, Jie Liu, Cui Tao

Published in: BMC Medical Informatics and Decision Making | Special Issue 2/2018

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Abstract

Background

The emergence of the deep convolutional neural network (CNN) greatly improves the quality of computer-aided supporting systems. However, due to the challenges of generating reliable and timely results, clinical adoption of computer-aided diagnosis systems is still limited. Recent informatics research indicates that machine learning algorithms need to be combined with sufficient clinical expertise in order to achieve an optimal result.

Methods

In this research, we used deep learning algorithms to help diagnose four common cutaneous diseases based on dermoscopic images. In order to facilitate decision-making and improve the accuracy of our algorithm, we summarized classification/diagnosis scenarios based on domain expert knowledge and semantically represented them in a hierarchical structure.

Results

Our algorithm achieved an accuracy of 87.25 ± 2.24% in our test dataset with 1067 images. The semantic summarization of diagnosis scenarios can help further improve the algorithm to facilitate future computer-aided decision support.

Conclusions

In this paper, we applied deep neural network algorithm to classify dermoscopic images of four common skin diseases and archived promising results. Based on the results, we further summarized the diagnosis/classification scenarios, which reflect the importance of combining the efforts of both human expertise and computer algorithms in dermatologic diagnoses.
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Metadata
Title
Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge
Authors
Xinyuan Zhang
Shiqi Wang
Jie Liu
Cui Tao
Publication date
01-07-2018
Publisher
BioMed Central
DOI
https://doi.org/10.1186/s12911-018-0631-9

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