Weighted average ensemble deep learning model for stratification of brain tumor in MRI images

Anand, Vatsala and Gupta, Sheifali and Gupta, Deepali and Gulzar, Yonis and Qin, Xin and Juneja, Sapna and Shah, Asadullah and Shaikh, Asadullah (2023) Weighted average ensemble deep learning model for stratification of brain tumor in MRI images. Diagnostics, 13 (7). pp. 1-13. E-ISSN 2075-4418

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Abstract

Abstract: Brain tumor diagnosis at an early stage can improve the chances of successful treatment and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures, such as magnetic resonance imaging (MRI), can be used to diagnose brain tumors. Deep learning, a type of artificial intelligence, can analyze MRI images in a matter of seconds, reducing the time it takes for diagnosis and potentially improving patient outcomes. Furthermore, an ensemble model can help increase the accuracy of classification by combining the strengths of multiple models and compensating for their individual weaknesses. Therefore, in this research, a weighted average ensemble deep learning model is proposed for the classification of brain tumors. For the weighted ensemble classification model, three different feature spaces are taken from the transfer learning VGG19 model, Convolution Neural Network (CNN) model without augmentation, and CNN model with augmentation. These three feature spaces are ensembled with the best combination of weights, i.e., weight1, weight2, and weight3 by using grid search. The dataset used for simulation is taken from The Cancer Genome Atlas (TCGA), having a lower-grade glioma collection with 3929 MRI images of 110 patients. The ensemble model helps reduce overfitting by combining multiple models that have learned different aspects of the data. The proposed ensemble model outperforms the three individual models for detecting brain tumors in terms of accuracy, precision, and F1-score. Therefore, the proposed model can act as a second opinion tool for radiologists to diagnose the tumor from MRI images of the brain.

Item Type:	Article (Journal)
Uncontrolled Keywords:	ensembled; weighted average; brain tumor; data augmentation; biomedical; Convolution Neural Network (CNN)
Subjects:	T Technology > T Technology (General) > T10.5 Communication of technical information
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Prof Asadullah Shah
Date Deposited:	18 Apr 2023 13:27
Last Modified:	18 Jan 2024 10:58
URI:	http://irep.iium.edu.my/id/eprint/104496

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