MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers

By Jaeyong Kang, Zahid Ullah, Jeonghwan Gwak. Italian National Conference on Sensors, 2021

502 citations recorded by the source corpus.

Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers. In our proposed framework, we adopt the concept of transfer learning and uses several pre-trained deep convolutional neural networks to extract deep features from brain magnetic resonance (MR) images. The extracted deep features are then evaluated by several machine learning classifiers. The top three deep features which perform well on several machine learning classifiers are selected and concatenated as an ensemble of deep features which is then fed into several machine learning classifiers to predict the final output. To evaluate the different kinds of pre-trained models as a deep feature extractor, machine learning classifiers, and the effectiveness of an ensemble of deep feature for brain tumor classification, we use three different brain magnetic resonance imaging (MRI) datasets that are openly accessible from the web. Experimental results demonstrate that an ensemble of deep features can help improving performance significantly, and in most cases, support vector machine (SVM) with radial basis function (RBF) kernel outperforms other machine learning classifiers, especially for large datasets.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/4d8f0ae904779a50b2e18fec49e51a5661a98d8a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mukhriddin Mukhiddinov
2. Akmalbek Bobomirzaevich Abdusalomov
3. T. Whangbo

In Computer Vision and Pattern Recognition

1. Mukhriddin Mukhiddinov
2. Akmalbek Bobomirzaevich Abdusalomov
3. T. Whangbo

In Neurology

1. Mukhriddin Mukhiddinov
2. Akmalbek Bobomirzaevich Abdusalomov
3. T. Whangbo

Citations in the corpus

• Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging (2023, methodology): “[40] using machine learning classifiers and a deep CNN feature ensemble.”