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Intelligent ultra-light deep learning model for multi-class brain tumor detection

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Qureshi, Shahzad Ahmad, Raza, Shan E. Ahmed, Hussain, Lal, Malibari, Areej A., Nour, Mohamed K., Rehman, Aziz ul, Al-Wesabi, Fahd N. and Hilal, Anwer Mustafa (2022) Intelligent ultra-light deep learning model for multi-class brain tumor detection. Applied Sciences, 12 (8). e3715. doi:10.3390/app12083715

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Official URL: https://doi.org/10.3390/app12083715

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Abstract

The diagnosis and surgical resection using Magnetic Resonance (MR) images in brain tumors is a challenging task to minimize the neurological defects after surgery owing to the non-linear nature of the size, shape, and textural variation. Radiologists, clinical experts, and brain surgeons examine brain MRI scans using the available methods, which are tedious, error-prone, time-consuming, and still exhibit positional accuracy up to 2−3 mm, which is very high in the case of brain cells. In this context, we propose an automated Ultra-Light Brain Tumor Detection (UL-BTD) system based on a novel Ultra-Light Deep Learning Architecture (UL-DLA) for deep features, integrated with highly distinctive textural features, extracted by Gray Level Co-occurrence Matrix (GLCM). It forms a Hybrid Feature Space (HFS), which is used for tumor detection using Support Vector Machine (SVM), culminating in high prediction accuracy and optimum false negatives with limited network size to fit within the average GPU resources of a modern PC system. The objective of this study is to categorize multi-class publicly available MRI brain tumor datasets with a minimum time thus real-time tumor detection can be carried out without compromising accuracy. Our proposed framework includes a sensitivity analysis of image size, One-versus-All and One-versus-One coding schemes with stringent efforts to assess the complexity and reliability performance of the proposed system with K-fold cross-validation as a part of the evaluation protocol. The best generalization achieved using SVM has an average detection rate of 99.23% (99.18%, 98.86%, and 99.67%), and F-measure of 0.99 (0.99, 0.98, and 0.99) for (glioma, meningioma, and pituitary tumors), respectively. Our results have been found to improve the state-of-the-art (97.30%) by 2%, indicating that the system exhibits capability for translation in modern hospitals during real-time surgical brain applications. The method needs 11.69 ms with an accuracy of 99.23% compared to 15 ms achieved by the state-of-the-art to earlier to detect tumors on a test image without any dedicated hardware providing a route for a desktop application in brain surgery.

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Brain -- Tumors -- Diagnosis, Brain -- Magnetic resonance imaging, Deep learning (Machine learning), Support vector machines, Image processing -- Digital techniques, Cancer -- Intraoperative radiotherapy
Journal or Publication Title: Applied Sciences
Publisher: MDPI
ISSN: 2076-3417
Official Date: 7 April 2022
Dates:
DateEvent
7 April 2022Published
29 March 2022Accepted
Volume: 12
Number: 8
Article Number: e3715
DOI: 10.3390/app12083715
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
RGP 2/18/43King Khalid Universityhttp://dx.doi.org/10.13039/501100007446
PNURSP2022R151Princess Nourah Bint Abdulrahman Universityhttp://dx.doi.org/10.13039/501100004242
22UQU4310373DSR07Umm Al-Qura Universityhttp://dx.doi.org/10.13039/501100006701
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