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

Nazik Alturki; Muhammad Umer; Abid Ishaq; Nihal Abuzinadah; Khaled Alnowaiser; Abdullah Mohamed; Oumaima Saidani; Imran Ashraf

doi:10.3390/cancers15061767

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

Cancers (Basel). 2023 Mar 14;15(6):1767. doi: 10.3390/cancers15061767.

Authors

Nazik Alturki¹, Muhammad Umer², Abid Ishaq², Nihal Abuzinadah³, Khaled Alnowaiser⁴, Abdullah Mohamed⁵, Oumaima Saidani¹, Imran Ashraf⁶

Affiliations

¹ Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
² Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan.
³ Faculty of Computer Science and Information Technology, King Abdulaziz University, P.O. Box. 80200, Jeddah 21589, Saudi Arabia.
⁴ Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
⁵ Research Centre, Future University in Egypt, New Cairo 11745, Egypt.
⁶ Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea.

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.

Grants and funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R333), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.