| Field |
Value |
Language |
|
dc.contributor.author |
Singh, SK |
|
|
dc.contributor.author |
Sinha, A |
|
|
dc.contributor.author |
Singh, H |
|
|
dc.contributor.author |
Mahanti, A |
|
|
dc.contributor.author |
Patel, A |
|
|
dc.contributor.author |
Mahajan, S |
|
|
dc.contributor.author |
Pandit, AK |
|
|
dc.contributor.author |
Varadarajan, V
https://orcid.org/0000-0003-3752-7220
|
|
|
dc.date.accessioned |
2025-01-15T02:56:39Z |
|
|
dc.date.available |
2025-01-15T02:56:39Z |
|
|
dc.date.issued |
2024-02 |
|
|
dc.identifier.citation |
MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83, (5), pp. 14173-14187 |
|
|
dc.identifier.issn |
1380-7501 |
|
|
dc.identifier.issn |
1573-7721 |
|
|
dc.identifier.uri |
http://hdl.handle.net/10453/183553
|
|
|
dc.description.abstract |
<jats:title>Abstract</jats:title><jats:p>In the western world,the prostate cancer is major cause of death in males. Magnetic Resonance Imaging (MRI) is widely used for the detection of prostate cancer due to which it is an open area of research. The proposed method uses deep learning framework for the detection of prostate cancer using the concept of Gleason grading of the historical images. A3D convolutional neural network has been used to observe the affected region and predicting the affected region with the help of Epithelial and the Gleason grading network. The proposed model has performed the state-of-art while detecting epithelial and the Gleason score simultaneously. The performance has been measured by considering all the slices of MRI, volumes of MRI with the test fold, and segmenting prostate cancer with help of Endorectal Coil for collecting the images of MRI of the prostate 3D CNN network. Experimentally, it was observed that the proposed deep learning approach has achieved overall specificity of 85% with an accuracy of 87% and sensitivity 89% over the patient-level for the different targeted MRI images of the challenge of the SPIE-AAPM-NCI Prostate dataset.</jats:p> |
|
|
dc.language |
English |
|
|
dc.publisher |
SPRINGER |
|
|
dc.relation.ispartof |
MULTIMEDIA TOOLS AND APPLICATIONS |
|
|
dc.relation.isbasedon |
10.1007/s11042-023-15793-0 |
|
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
|
dc.subject |
0801 Artificial Intelligence and Image Processing, 0803 Computer Software, 0805 Distributed Computing, 0806 Information Systems |
|
|
dc.subject.classification |
Artificial Intelligence & Image Processing |
|
|
dc.subject.classification |
Software Engineering |
|
|
dc.subject.classification |
4009 Electronics, sensors and digital hardware |
|
|
dc.subject.classification |
4603 Computer vision and multimedia computation |
|
|
dc.subject.classification |
4605 Data management and data science |
|
|
dc.subject.classification |
4606 Distributed computing and systems software |
|
|
dc.title |
A novel deep learning-based technique for detecting prostate cancer in MRI images |
|
|
dc.type |
Journal Article |
|
|
utslib.citation.volume |
83 |
|
|
utslib.for |
0801 Artificial Intelligence and Image Processing |
|
|
utslib.for |
0803 Computer Software |
|
|
utslib.for |
0805 Distributed Computing |
|
|
utslib.for |
0806 Information Systems |
|
|
pubs.organisational-group |
University of Technology Sydney |
|
|
pubs.organisational-group |
University of Technology Sydney/Faculty of Engineering and Information Technology |
|
|
pubs.organisational-group |
University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science |
|
|
utslib.copyright.status |
open_access |
* |
|
dc.rights.license |
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/ |
|
|
dc.date.updated |
2025-01-15T02:56:38Z |
|
|
pubs.issue |
5 |
|
|
pubs.publication-status |
Published |
|
|
pubs.volume |
83 |
|
|
utslib.citation.issue |
5 |
|
