MRI-Based Effective Ensemble Frameworks for Predicting Human Brain Tumor

• Published in: Journal of imaging Vol. 9; no. 8; p. 163
• Main Authors: Khan, Farhana, Ayoub, Shahnawaz, Gulzar, Yonis, Majid, Muneer, Reegu, Faheem Ahmad, Mir, Mohammad Shuaib, Soomro, Arjumand Bano, Elwasila, Osman
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2023; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Artificial neural networks; Brain; Brain cancer; Brain research; brain tumor; Brain tumors; Classification; convolution neural network; Deep learning; Diagnosis; ensemble approach; Feature extraction; Machine learning; Magnetic resonance imaging; Medical imaging; Neural networks; Neuroimaging; Spectrum analysis; Support vector machines; Tumors; Wavelet transforms; X-rays; India
• ISSN: 2313-433X; 2313-433X
• DOI: 10.3390/jimaging9080163

The diagnosis of brain tumors at an early stage is an exigent task for radiologists. Untreated patients rarely survive more than six months. It is a potential cause of mortality that can occur very quickly. Because of this, the early and effective diagnosis of brain tumors requires the use of an automated method. This study aims at the early detection of brain tumors using brain magnetic resonance imaging (MRI) data and efficient learning paradigms. In visual feature extraction, convolutional neural networks (CNN) have achieved significant breakthroughs. The study involves features extraction by deep convolutional layers for the efficient classification of brain tumor victims from the normal group. The deep convolutional neural network was implemented to extract features that represent the image more comprehensively for model training. Using deep convolutional features helps to increase the precision of tumor and non-tumor patient classifications. In this paper, we experimented with five machine learnings (ML) to heighten the understanding and enhance the scope and significance of brain tumor classification. Further, we proposed an ensemble of three high-performing individual ML models, namely Extreme Gradient Boosting, Ada-Boost, and Random Forest (XG-Ada-RF), to derive binary class classification output for detecting brain tumors in images. The proposed voting classifier, along with convoluted features, produced results that showed the highest accuracy of 95.9% for tumor and 94.9% for normal. Compared to individual methods, the proposed ensemble approach demonstrated improved accuracy and outperformed the individual methods.