Ms. Sabrina Iddir Profile

Sabrina Iddir

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 | 13 March 2024 Presentation + Paper

Deep learning for automated diagnosis of uveal melanoma

Albert Dadzie, Sabrina Iddir, Mansour Abtahi, Behrouz Ebrahimi, David Le, Taeyoon Son, Michael Heiferman, Xincheng Yao

Proceedings Volume 12824, 128240B (2024) https://doi.org/10.1117/12.3000655

KEYWORDS: Deep learning, Tumors, Data fusion, Image fusion, Melanoma, Visualization, Data modeling, Eye models, Performance modeling, Diagnostics

Read Abstract +

Accurate differentiation of uveal melanoma and choroidal nevi is critical for optimal patient care, preventing unnecessary procedures for benign lesions while ensuring timely intervention for potentially malignant cases. This study aimed to validate deep learning classification of these lesions and to evaluate the impact of different color fusion options on classification performance. To evaluate the effect of color fusion options on the classification performance, we tested early fusion, intermediate fusion, and late fusion using ultra-widefield retinal images. Specificity, sensitivity, F1-score, accuracy, and the area under the curve (AUC) of a receiver operating characteristic (ROC) were used to assess the performance of the deep learning model. The results show that the color fusion options significantly impacted the deep learning classification performance, with intermediate fusion emerging as the best strategy, outperforming both single-color learning and the other fusion strategies. The intermediate fusion strategy had an accuracy of 89.72%, sensitivity of 85.05%, specificity of 91.64, F1 score of 0.8492 and an AUC of 0.9335. These compelling results emphasize the vast potential of deep learning to enhance the accuracy of diagnosis and classification of UM and choroidal nevi, leading to improved patient outcomes and optimized treatment strategies. By harnessing the power of deep learning and color fusion strategies, this study not only provides valuable insights into the application of these approaches in the field of ophthalmology but also highlights their critical significance in automating the classification of UM and choroidal nevi.

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