Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Mar 14;15(6):1767.
doi: 10.3390/cancers15061767.

Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification

Nazik Alturki  1 , Muhammad Umer  2 , Abid Ishaq  2 , Nihal Abuzinadah  3 , Khaled Alnowaiser  4 , Abdullah Mohamed  5 , Oumaima Saidani  1 , Imran Ashraf  6
Affiliations

Combining CNN Features with Voting Classifiers for Optimizing Performance of Brain Tumor Classification

Nazik Alturki et al. Cancers (Basel). .

Abstract

Brain tumors and other nervous system cancers are among the top ten leading fatal diseases. The effective treatment of brain tumors depends on their early detection. This research work makes use of 13 features with a voting classifier that combines logistic regression with stochastic gradient descent using features extracted by deep convolutional layers for the efficient classification of tumorous victims from the normal. From the first and second-order brain tumor features, deep convolutional features are extracted for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classification. The proposed voting classifier along with convoluted features produces results that show the highest accuracy of 99.9%. Compared to cutting-edge methods, the proposed approach has demonstrated improved accuracy.

Keywords: brain tumor prediction; deep convolutional features; ensemble learning; healthcare.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interests.

Figures

Figure 1
Figure 1
Architecture diagram of the CNN with voting classifier (LR+SGD) model.
Figure 2
Figure 2
Workflow diagram of the proposed voting classifier (LR+SGD) model.
Figure 3
Figure 3
Architecture of the proposed voting classifier (LR+SGD) model.
See this image and copyright information in PMC

References

    1. Umer M., Naveed M., Alrowais F., Ishaq A., Hejaili A.A., Alsubai S., Eshmawi A., Mohamed A., Ashraf I. Breast Cancer Detection Using Convoluted Features and Ensemble Machine Learning Algorithm. Cancers. 2022;14:6015. doi: 10.3390/cancers14236015. - DOI - PMC - PubMed
    1. Amin J., Sharif M., Raza M., Saba T., Anjum M.A. Brain tumor detection using statistical and machine learning method. Comput. Methods Programs Biomed. 2019;177:69–79. doi: 10.1016/j.cmpb.2019.05.015. - DOI - PubMed
    1. McFaline-Figueroa J.R., Lee E.Q. Brain tumors. Am. J. Med. 2018;131:874–882. doi: 10.1016/j.amjmed.2017.12.039. - DOI - PubMed
    1. Arvold N.D., Lee E.Q., Mehta M.P., Margolin K., Alexander B.M., Lin N.U., Anders C.K., Soffietti R., Camidge D.R., Vogelbaum M.A., et al. Updates in the management of brain metastases. Neuro-oncology. 2016;18:1043–1065. doi: 10.1093/neuonc/now127. - DOI - PMC - PubMed
    1. Saba T., Mohamed A.S., El-Affendi M., Amin J., Sharif M. Brain tumor detection using fusion of hand crafted and deep learning features. Cogn. Syst. Res. 2020;59:221–230. doi: 10.1016/j.cogsys.2019.09.007. - DOI

LinkOut - more resources