Semantic Segmentation Using Deep Learning for Brain Tumor MRI via Fully Convolution Neural Networks

Kumar, Sanjay; Negi, Ashish; Singh, J. N.

doi:10.1007/978-981-13-1742-2_2

Cited by 25 publications

(3 citation statements)

References 13 publications

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“…It has an input layer, several hidden layers (convolutional, normalization, and pooling repeats), fully linked and output layer. Layer of output neurons in one layer communicate with neurons in the next layer, allowing for easier scaling of higherresolution pictures [28]. The pooling or sub-sampling operations may be employed to decrease the input dimensions.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Recent Trends on Brain Tumor Detection Using Hybrid Deep Learning Methods

Buchade,

Kantipudi

2024

RIA

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The term "brain tumor" describes the unregulated increase in brain cells, which can have various adverse consequences. In the field of medical research, a variety of methods are employed to find brain tumor and the most reliable method still utilized by specialists is Magnetic Resonance Imaging (MRI). The noninvasive MRI method has developed into a primary emission brain tumor investigative tool. In order to accurately identify the extent of tumor, reliable, entirely an automatic segmentation method for the brain tumor and this is still being investigated. There is a higher possibility of success for the treatment when tumors are found early. Detecting brain tumor affected cells is tedious and time-consuming process. Identification and classification of brain tumors at the earliest is very essential for effective treatment. This article conducted an analysis of existing methodologies to apply various forms of deep learning techniques to MRI data. This review provides hybrid deep learning based brain tumor diagnosis approach which combines different deep learning methods like Convolutional Neural Networks (CNN), UNET Architecture, GoogLeNet and Gabor Filter for feature extraction. From extensive survey, this review concludes that deep learning approaches provide more accurate and efficient results than traditional machine learning algorithms. This survey highlights the current clinical challenges, potential future solutions and opens up the researcher's challenges to evolve systematic brain tumor detection system demonstrating clinically acceptable better accuracy which will assist the radiologists in diagnosis.

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Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Recent Trends on Brain Tumor Detection Using Hybrid Deep Learning Methods

Buchade,

Kantipudi

2024

RIA

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“…A recent paper entitled "Semantic segmentation using deep learning for brain tumor MRI via fully convolution neural networks" by Kumar et al, states that early head lump perception along with analysis might make serious medical problems to clinic (Kumar et al, 2019). In this research paper, two significant architectures for brain tumor segmentation were developed and their precision on pinnacle BraTS confront 2017 dataset were evaluated, along with as well investigate the role of transport knowledge beginning the BraTS architecture to the Rembrandt dataset.…”

Section: Fields Of Learning Algorithms In Biomedical Engineeringmentioning

confidence: 99%

Machine Learning and Deep Learning in Biomedical Engineering

Gzawi¹,

Ababeneh²

2022

IJSRP

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Machine learning as well as Deep learning applications had qualified huge growth in different medical fields such as medical image analysis as well as other related data due to the fact of the convenience of several data sets to train the learning algorithms in multimodal modes. However, machine learning and deep learning can distinguish patterns in healthcare data to enhance diagnosis and prognosis. The most utilized machine learning and deep learning techniques for healthcare applications are autoencoder, restricted Boltzmann machine, deep belief network, recurrent neural network, convolutional neural network, generative adversarial network, neural networks, and support vector machine. This paper aimed to illustrate the different applications associated with these learning algorithms that focus on healthcare field. It also illustrates the high-tech methods utilized to employ the learning algorithms.

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“…Three-dimensional CNN is used for segmentation of the brain tumor. S. Kumar uses UNET and crops the image when fed into the network for better results [100]. The interactive deep-learning-based framework consists of the integration of CNNs into the bounding box and the scribble-based image segmentation pipeline is developed by G. Wang for tumor segmentation.…”

Section: Brain Tumor Segmentationmentioning

confidence: 99%

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

et al. 2020

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Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.

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Semantic Segmentation Using Deep Learning for Brain Tumor MRI via Fully Convolution Neural Networks

Cited by 25 publications

References 13 publications

Recent Trends on Brain Tumor Detection Using Hybrid Deep Learning Methods

Recent Trends on Brain Tumor Detection Using Hybrid Deep Learning Methods

Machine Learning and Deep Learning in Biomedical Engineering

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

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