Feature-versus deep learning-based approaches for the automated detection of brain tumor with magnetic resonance images: A comparative study

U. Raghavendra; Anjan Gudigar; Tejaswi N. Rao; V. Rajinikanth; Edward J. Ciaccio; Chai Hong Yeong; Suresh Chandra Satapathy; Filippo Molinari; U. Rajendra Acharya

doi:10.1002/ima.22646

Feature-versus deep learning-based approaches for the automated detection of brain tumor with magnetic resonance images: A comparative study

U. Raghavendra, Anjan Gudigar, Tejaswi N. Rao, V. Rajinikanth, Edward J. Ciaccio, Chai Hong Yeong, Suresh Chandra Satapathy, Filippo Molinari, U. Rajendra Acharya

Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

Abstract

The public health is significantly affected by development of brain tumors in human patients. Glioblastoma (GBM) is a relatively common, malignant form of brain tumor, which is currently challenging to treat and cure. In contrast, Lower Grade Gliomas (LGGs) originate from glial cells and can mostly be treated and cured in the initial stages if they are detected early. A computer-aided diagnostics (CAD) tool may help to test for the presence and extent of any such tumor, and thus can be assistive in the clinical diagnostic process. Herein, we compare handcrafted versus non-handcrafted features-based CAD to characterize GBM and LGG. Our machine learning-based handcrafted model uses quantitative techniques of enhanced elongated quinary patterns and entropies analysis. We have also developed a non-handcrafted deep learning model using Visual Geometry Group-16 architecture for segregating GBM and LGG subjects results in 94.25% accuracy using k-nearest neighbor classifier.

Original language	English
Pages (from-to)	501 - 516
Journal	International Journal of Imaging Systems and Technology
Volume	32
Issue number	2
DOIs	https://doi.org/10.1002/ima.22646
Publication status	Published - 03-2022

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Software
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1002/ima.22646

Cite this

Raghavendra, U., Gudigar, A., Rao, T. N., Rajinikanth, V., Ciaccio, E. J., Yeong, C. H., Satapathy, S. C., Molinari, F., & Acharya, U. R. (2022). Feature-versus deep learning-based approaches for the automated detection of brain tumor with magnetic resonance images: A comparative study. International Journal of Imaging Systems and Technology, 32(2), 501 - 516. https://doi.org/10.1002/ima.22646

@article{41c77403629846cd848a421f1efd7057,

title = "Feature-versus deep learning-based approaches for the automated detection of brain tumor with magnetic resonance images: A comparative study",

abstract = "The public health is significantly affected by development of brain tumors in human patients. Glioblastoma (GBM) is a relatively common, malignant form of brain tumor, which is currently challenging to treat and cure. In contrast, Lower Grade Gliomas (LGGs) originate from glial cells and can mostly be treated and cured in the initial stages if they are detected early. A computer-aided diagnostics (CAD) tool may help to test for the presence and extent of any such tumor, and thus can be assistive in the clinical diagnostic process. Herein, we compare handcrafted versus non-handcrafted features-based CAD to characterize GBM and LGG. Our machine learning-based handcrafted model uses quantitative techniques of enhanced elongated quinary patterns and entropies analysis. We have also developed a non-handcrafted deep learning model using Visual Geometry Group-16 architecture for segregating GBM and LGG subjects results in 94.25% accuracy using k-nearest neighbor classifier.",

author = "U. Raghavendra and Anjan Gudigar and Rao, {Tejaswi N.} and V. Rajinikanth and Ciaccio, {Edward J.} and Yeong, {Chai Hong} and Satapathy, {Suresh Chandra} and Filippo Molinari and Acharya, {U. Rajendra}",

note = "Publisher Copyright: {\textcopyright} 2021 Wiley Periodicals LLC.",

year = "2022",

month = mar,

doi = "10.1002/ima.22646",

language = "English",

volume = "32",

pages = "501 -- 516",

journal = "International Journal of Imaging Systems and Technology",

issn = "0899-9457",

publisher = "John Wiley and Sons Inc.",

number = "2",

}

Raghavendra, U , Gudigar, A, Rao, TN, Rajinikanth, V, Ciaccio, EJ, Yeong, CH, Satapathy, SC, Molinari, F & Acharya, UR 2022, 'Feature-versus deep learning-based approaches for the automated detection of brain tumor with magnetic resonance images: A comparative study', International Journal of Imaging Systems and Technology, vol. 32, no. 2, pp. 501 - 516. https://doi.org/10.1002/ima.22646

TY - JOUR

T1 - Feature-versus deep learning-based approaches for the automated detection of brain tumor with magnetic resonance images

T2 - A comparative study

AU - Raghavendra, U.

AU - Gudigar, Anjan

AU - Rao, Tejaswi N.

AU - Rajinikanth, V.

AU - Ciaccio, Edward J.

AU - Yeong, Chai Hong

AU - Satapathy, Suresh Chandra

AU - Molinari, Filippo

AU - Acharya, U. Rajendra

PY - 2022/3

Y1 - 2022/3

N2 - The public health is significantly affected by development of brain tumors in human patients. Glioblastoma (GBM) is a relatively common, malignant form of brain tumor, which is currently challenging to treat and cure. In contrast, Lower Grade Gliomas (LGGs) originate from glial cells and can mostly be treated and cured in the initial stages if they are detected early. A computer-aided diagnostics (CAD) tool may help to test for the presence and extent of any such tumor, and thus can be assistive in the clinical diagnostic process. Herein, we compare handcrafted versus non-handcrafted features-based CAD to characterize GBM and LGG. Our machine learning-based handcrafted model uses quantitative techniques of enhanced elongated quinary patterns and entropies analysis. We have also developed a non-handcrafted deep learning model using Visual Geometry Group-16 architecture for segregating GBM and LGG subjects results in 94.25% accuracy using k-nearest neighbor classifier.

AB - The public health is significantly affected by development of brain tumors in human patients. Glioblastoma (GBM) is a relatively common, malignant form of brain tumor, which is currently challenging to treat and cure. In contrast, Lower Grade Gliomas (LGGs) originate from glial cells and can mostly be treated and cured in the initial stages if they are detected early. A computer-aided diagnostics (CAD) tool may help to test for the presence and extent of any such tumor, and thus can be assistive in the clinical diagnostic process. Herein, we compare handcrafted versus non-handcrafted features-based CAD to characterize GBM and LGG. Our machine learning-based handcrafted model uses quantitative techniques of enhanced elongated quinary patterns and entropies analysis. We have also developed a non-handcrafted deep learning model using Visual Geometry Group-16 architecture for segregating GBM and LGG subjects results in 94.25% accuracy using k-nearest neighbor classifier.

UR - http://www.scopus.com/inward/record.url?scp=85113194865&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85113194865&partnerID=8YFLogxK

U2 - 10.1002/ima.22646

DO - 10.1002/ima.22646

M3 - Article

AN - SCOPUS:85113194865

SN - 0899-9457

VL - 32

SP - 501

EP - 516

JO - International Journal of Imaging Systems and Technology

JF - International Journal of Imaging Systems and Technology

IS - 2

ER -

Feature-versus deep learning-based approaches for the automated detection of brain tumor with magnetic resonance images: A comparative study

Abstract

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this