Mr. Elmoatazbellah Ben Omar Profile

Elmoatazbellah Ben Omar

at Wright State Univ

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 | 7 June 2024 Poster + Paper

Deep learning architecture: an application for skin lesion segmentation-based polar image transformations on dermoscopy images

Redha Ali, Almabrok Essa, Elmoatazbellah Ben Omar, Adel Alshamili, Fatmah Ali, Russell Hardie

Proceedings Volume 13040, 130400J (2024) https://doi.org/10.1117/12.3014731

KEYWORDS: Image segmentation, Skin, Education and training, Transformers, Deep learning, Data modeling, Performance modeling, Medical imaging, Skin cancer, RGB color model

Read Abstract +

Accurately segmenting skin lesions from dermoscopy images is crucial for improving the quantitative analysis of skin cancer. However, segmenting melanoma automatically is difficult due to the significant variation in melanoma and unclear boundaries of the lesion areas. While Convolutional Neural Networks (CNNs) have made impressive progress in this area, most existing solutions need help to effectively capture global dependencies resulting from limited receptive fields. Recently, transformers have emerged as a promising tool for modeling global context by using a powerful global and local attention mechanism. In this paper, we investigated the effectiveness of various deep learning, including CNN-based and transform-based approaches, for the segmentation of skin lesions on dermoscopy images. We also studied and compared the performance of transfer learning algorithms developed based on well-established encoders such as Swin Transformer, Mix-Transformer, Vision Transformer, ResNet, VGG-16, and DenseNet. Our proposed approach involves training a neural network on polar transformations of the original dataset, with the polar origin set to the object’s center point. This simplifies the segmentation and localization tasks and reduces dimensionality, making it easier for the network to converge. The ISIC 2018 datasets containing 2,594 dermoscopy images with their ground truth segmentation masks was used in the evaluation of our approach for skin lesion segmentation tasks. This dataset was randomly split into 70%, 10%, and 20% groups for training, validation, and testing purposes. The experimental results showed that when we used polar transformations as a pre-processing step, the CNN-based and transform-based approaches generally improved the models efficiency across dataset.

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