Sunil Maharjan1, Abeer Alsadoon2, P W C Prasad1, Thair Al-Dalain1, Omar Hisham Alsadoon3. 1. Charles Sturt University, Sydney, Australia. 2. Charles Sturt University, Sydney, Australia. Electronic address: aalsadoon@studygroup.com. 3. Department of Islamic Sciences, Al Iraqia University, Baghdad, Iraq.
Abstract
BACKGROUND AND AIM: In deep learning, the sigmoid function is unsuccessfully used for the multiclass classification of the brain tumour due to its limit of binary classification. This study aims to increase the classification accuracy by reducing the risk of overfitting problem and supports multi-class classification. The proposed system consists of a convolutional neural network with modified softmax loss function and regularization. RESULTS: Classification accuracy for the different types of tumours and the processing time were calculated based on the probability score of the labeled data and their execution time. Different accuracy values and processing time were obtained when testing the proposed system using different samples of MRI images. The result shows that the proposed solution is better compared to the other systems. Besides, the proposed solution has higher accuracy by almost 2 % and less processing time of 40∼50 ms compared to other current solutions. CONCLUSION: The proposed system focused on classification accuracy of the different types of tumours from the 3D MRI images. This paper solves the issues of binary classification, the processing time, and the issues of overfitting of the data.
BACKGROUND AND AIM: In deep learning, the sigmoid function is unsuccessfully used for the multiclass classification of the brain tumour due to its limit of binary classification. This study aims to increase the classification accuracy by reducing the risk of overfitting problem and supports multi-class classification. The proposed system consists of a convolutional neural network with modified softmax loss function and regularization. RESULTS: Classification accuracy for the different types of tumours and the processing time were calculated based on the probability score of the labeled data and their execution time. Different accuracy values and processing time were obtained when testing the proposed system using different samples of MRI images. The result shows that the proposed solution is better compared to the other systems. Besides, the proposed solution has higher accuracy by almost 2 % and less processing time of 40∼50 ms compared to other current solutions. CONCLUSION: The proposed system focused on classification accuracy of the different types of tumours from the 3D MRI images. This paper solves the issues of binary classification, the processing time, and the issues of overfitting of the data.
Authors: Amin Zadeh Shirazi; Eric Fornaciari; Mark D McDonnell; Mahdi Yaghoobi; Yesenia Cevallos; Luis Tello-Oquendo; Deysi Inca; Guillermo A Gomez Journal: J Pers Med Date: 2020-11-12