The method selects the most relevant features and segments edema and tumor using a classification algorithm based on Multiple Kernel Learning (MKL). Using MKL ...

Compared to single-kernel methods, classification methods based on multikernel strategies and MKL are more conducive to enhancing the interpretability of ...

In the brain tumor segmentation problem, we are asked to distinguish tumor tissues such as edema and active tumor from normal brain tissues such as gray matter, ...

Using MKL algorithm, we can associate one or more kernels to each feature. Each kernel is associated to a weight reflecting its importance in the classification ...

Bibliographic details on Feature selection and classification using multiple kernel learning for brain tumor segmentation.

Dec 11, 2023 · The ensemble kernel employed in this study is specifically designed to classify glioma, meningioma, and pituitary tumours. Its implementation ...

Oct 22, 2024 · This method involves two steps: categorizing the tumor area with a multi-kernel SVM algorithm that works on many image bases and produces ...

Supervised machine learning-based brain tumor segmentation approaches transformed the image segmentation problem into a tumorous pixel classification problem.

In this review paper, we discuss the most effective segmentation techniques based on the datasets that are widely used and publicly available.

Our proposed deep learning model showed promising results, accurately identifying the presence and precise location of brain tumors in MRI images.