Automated classification of MRI based on hybrid Least Square Support Vector Machine and Chaotic PSO

Sivapriya, T. R.; Kamal, Ahmad; Thavavel, V.

doi:10.1109/icccnt.2012.6396019

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…A. Shenbagarajan, et al [52] produced better classification accuracy on the Levenberg-Marquardt (LM) algorithm. D. Jude Hemanth, J. Anitha [53] generated Modified GA approaches which reduce the number of features. That will result in the system's reduced complexity.…”

Section: Classificationmentioning

confidence: 99%

Survey of brain tumor segmentation with deep neural networks

Kumar¹,

Sarath²

2022

ijhs

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A brain tumour is a serious condition that, if not diagnosed and treated early on, can lead to death. Researchers have proposed a variety of traditional and recently developed deep learning based segmentation and classification approaches for determining the condition of the tumor. Deep learning is found to be efficient and robust for classification and segmentation as it detects the fine-to-coarse information about the tumors. The main component of deep learning is layered neural network architecture popularly known as convolutional neural network. Distinct information from brain images can be acquired and analysed depending on the architecture. In order to achieve high segmentation and classification accuracy, more research is required in this area. In this paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation and deep learning neural networks, clearly highlighting their building blocks and different strategies. Finally, this article implying about present status on segmentation and classification of tumor-based image processing through deep learning models.

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Section: Classificationmentioning

confidence: 99%