Mr. Cheng Zhang Profile

Cheng Zhang

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 | 15 November 2022 Paper

Drug screening methods based on deep learning

Cheng Zhang, Jianhua Wang, Hongxin Lin, Zhenlin Zhan, Lisheng Lin, Jian Sui

Proceedings Volume 12448, 124480V (2022) https://doi.org/10.1117/12.2638221

KEYWORDS: Data modeling, Machine learning, Phosphorus, Feature extraction, Image classification, Performance modeling, Laser irradiation, Image segmentation, Microscopes, Image processing

Read Abstract +

Drug screening is an important step in the development of new drugs. Through appropriate experimental methods and screening models, drugs with specific bioactivity can be transferred from laboratory research to clinical application. Nowadays, traditional efficiency of drug experimentation has been unable to meet the needs of our society. With the rapid development of computer technology, computer-assisted diagnosis and treatment have been gradually accepted and recognized by clinicians and patients. Drug screening process at the cellular level was studied in this paper. We not only compared the advantages and disadvantages of deep learning models and traditional machine learning methods, but also analyzed the performance of different deep learning models. First, Hela cells injected with different anti-stress drugs were divided into groups for experiment. G3BP, TIA-1 and the nucleus were labeled, respectively. The images were obtained using a single-photon microscope. Then, we distinguished the images of Hela cells treated with different drugs through visual observation, traditional machine learning (LBP/Gabor+SVM) and deep learning algorithms (VGGNet, GoogLeNet, ResNext and DenseNet), respectively. Experimental results showed that compared with visual observation, traditional machine learning and deep learning algorithms had better objectivity. Furthermore, deep learning models all had good classification performance. The highest average correct recognition rate was 92.97%, while that of the traditional method was only 80.93%. Therefore, drug screening methods based on deep learning could assist in screening the optimal treatment drugs, which help clinicians choose appropriate therapy.

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