dc.contributor.author |
Kaur, Deepinder |
|
dc.contributor.author |
Singh, Jaspreet |
|
dc.date.accessioned |
2024-06-14T19:01:39Z |
|
dc.date.available |
2024-06-14T19:01:39Z |
|
dc.date.issued |
2024-06-14 |
|
dc.identifier.issn |
2210-142X |
|
dc.identifier.uri |
https://journal.uob.edu.bh:443/handle/123456789/5755 |
|
dc.description.abstract |
Brain metastases (BM), which affect 10-30% of cancer patients, represent important diagnostic and therapeutic problems due
to their impact on cognitive function. Traditional manual MRI interpretation methods are time-consuming and potentially inaccurate,
especially for tiny or diverse tumours. Artificial intelligence (AI) tools such as deep learning (DL) and machine learning (ML) made it
possible to analyse complex MRI data quickly, accurately, and automatically, which was a major factor in BM diagnosis. This paper
presents a novel approach for automatic brain metastases segmentation on MRI data that makes use of a U-Net model. To improve
the accuracy of BM identification, the proposed method combines numerous imaging modalities, including T1-Weighted, T2-Weighted,
T1-contrast enhanced, and Fluid-Attenuated Inversion Recovery (FLAIR). The University of California San Francisco Brain Metastases
Stereotactic Radiosurgery (UCSF-BMSR) MRI dataset has been utilized for this purpose. The U-Net model was trained, verified, and
tested on this dataset, and it performed admirably with an overall accuracy of 99.75%, a dice coefficient of 64.49%, and an Intersection
over Union (IOU) of 96.81%. The proposed technique has been compared with two baseline models, namely Convolutional Neural
Networks (CNN) and Fully Convolutional Networks (FCN). The U-Net model outperformed the baselines in all important measures,
demonstrating its potential for real-world clinical application. The findings highlight the U-Net model’s capacity to greatly enhance BM
detection accuracy, allowing for prompt treatment decisions. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
University of Bahrain |
en_US |
dc.subject |
Brain Metastasis, Brain Metastases, U Net, Segmentation, BM Detection |
en_US |
dc.title |
Design, Development and Evaluation of a Deep Learning-Based Personalized Healthcare System for Diagnosis of Brain Metastases |
en_US |
dc.identifier.doi |
http://dx.doi.org/10.12785/ijcds/XXXXXX |
|
dc.volume |
16 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
189 |
en_US |
dc.pageend |
198 |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authoraffiliation |
Department of Computer Science and Engineering,Chandigarh University |
en_US |
dc.contributor.authoraffiliation |
Department of Computer Science and Engineering,Chandigarh University |
en_US |
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |