Mr. Minghao Li Profile

Minghao Li

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 1 December 2021 Paper

Automatic skin cancer detection and classification based on convolutional neural network and natural language processing

Kewei Chen, Minghao Li, Zhimo Li, Yunpeng Tao

Proceedings Volume 12079, 120792G (2021) https://doi.org/10.1117/12.2623079

KEYWORDS: Data modeling, Skin cancer, Convolutional neural networks, Tumor growth modeling, Cancer, Performance modeling, Process modeling, Melanoma, Diagnostics, Artificial intelligence

Read Abstract +

Skin cancer has been one of the most common forms of cancer around the world. Due to the efficiency and high accuracy of artificial intelligence, more and more hospitals are using it to assist doctors in finding out cancer quickly. In our work, we trained a convolutional neural network with a dataset called ‘Skin Cancer ISIC’ to detect appearances of 9 different kinds of skin cancers. Firstly, we trained a convolutional neural network model with the original data from the dataset. It contains several convolutional and max pooling layers, and its accuracy achieved 45%. Although the classification of 9 species will undoubtedly decrease the accuracy of the model compared to making fewer species, 45% is not an acceptable value for medical judgment. To improve the accuracy of our model, we used data enhancement and retrained our model. During the training of the dataset, we find out that the pictures of the dataset are not in the same standard. The brightness, contrast, size, and shape of the images are different, which increased the difficulty of learning. By rotating pictures, adjust brightness, equalizing histogram, adding random noise, flip and adding random USM, we reach an accuracy of 60%. Moreover, we employed ResNet-50, a pre-training model, as the convolutional neural network model to further improve our accuracy. The final testing result gets an accuracy over 65 %, which is a huge improvement from the beginning.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years