Fusing Multiple Deep Models for <i>In Vivo</i> Human Brain Hyperspectral Image Classification to Identify Glioblastoma Tumor

Hao, Qiaobo; Yu, Pei; Rong, Zhou; Sun, Bin; Sun, Jun; Li, Shutao; Kang, Xudong

doi:10.1109/tim.2021.3117634

Cited by 38 publications

(15 citation statements)

References 44 publications

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“…Hao et al [ 21 ] proposed a glioblastoma brain tumor classification model using HSI images. The spatial and spectral features of HSI images were used by implementing various deep learning models for the detection of brain tumor.…”

Section: Related Workmentioning

confidence: 99%

Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms

Rinesh

Maheswari²,

Arthi

et al. 2022

Journal of Healthcare Engineering

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The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous image models. This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. The tumor is located using the combination of k-based clustering processes like k-nearest neighbour and k-means clustering. The value of k in both methods is determined using the optimization process called the firefly algorithm. The optimization processes reduce the manual calculation for finding K’s optimal value to segment the brain regions. The labelling of the areas of the brain is done using the multilayer feedforward neural network. The proposed technique produced better results than the existing methods like hybrid k-means clustering and parallel k-means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute error value. The proposed model achieved 96.47% accuracy, 96.32% sensitivity, and 98.24% specificity, which are improved compared to other techniques.

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Section: Related Workmentioning

confidence: 99%

Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms

Rinesh

Maheswari²,

Arthi

et al. 2022

Journal of Healthcare Engineering

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“…The 3D–2D hybrid CNN achieves the best results with a mean accuracy of 80%, sensitivity of 76, 68, 74, 96%, specificity of 87, 98, 92, 87%, and AUC of 78, 70, 84, 91%, for normal, tumor, blood vessels and background, respectively ( Manni et al, 2020 ). Furthermore, Hao, et al reported a multiple deep model fusion (include three neural networks) based extraction method to achieve an overall accuracy of 96.34% for the identification of GBM tumors ( Hao et al, 2021 ). This method employed 1-D deep neural network (1D-DNN) and 2-D convolution neural network (2D-CNN) to extract spectral characteristics and spectral spatial characteristic for the HSI classification of human brain.…”

Section: Hyperspectral Imaging In Cerebral Diagnosismentioning

confidence: 99%

Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery

Yang

et al. 2022

Front. Bioeng. Biotechnol.

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The study of brain science is vital to human health. The application of hyperspectral imaging in biomedical fields has grown dramatically in recent years due to their unique optical imaging method and multidimensional information acquisition. Hyperspectral imaging technology can acquire two-dimensional spatial information and one-dimensional spectral information of biological samples simultaneously, covering the ultraviolet, visible and infrared spectral ranges with high spectral resolution, which can provide diagnostic information about the physiological, morphological and biochemical components of tissues and organs. This technology also presents finer spectral features for brain imaging studies, and further provides more auxiliary information for cerebral disease research. This paper reviews the recent advance of hyperspectral imaging in cerebral diagnosis. Firstly, the experimental setup, image acquisition and pre-processing, and analysis methods of hyperspectral technology were introduced. Secondly, the latest research progress and applications of hyperspectral imaging in brain tissue metabolism, hemodynamics, and brain cancer diagnosis in recent years were summarized briefly. Finally, the limitations of the application of hyperspectral imaging in cerebral disease diagnosis field were analyzed, and the future development direction was proposed.

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“…rich spectral signature [1]. Therefore, HSI data containing a large amount of information have been successfully applied in environment monitoring [2], [3], medical treatment [4], [5], agricultural evaluation [6], and geological exploration [7]. These applications are premised on the precise classification of each pixel in the HSI.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Adaptive Convolution for Hyperspectral Image Classification

Ren

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Convolutional neural network (CNN) is widely used in HSI classification owing to their advantages of spatial-spectral features capture capability, learning depth features as well as their structural flexibility. Nevertheless, the shape of the convolution kernel is fixed, a limitation that leads to shape fixation when modeling different feature in CNN, especially in the edge regions between classes. A multiscale adaptive convolution (MSAC) model is proposed in this paper to overcome this shortcoming. Combining superpixels with the traditional convolutional kernels to form adaptive kernels automatically adjusts the receptive field, suppresses edge noise, and enhances feature learning for different classes. On the basis of adaptive convolution, adaptive convolution units (AConvUs) are constructed. The hierarchical residual structure is constructed by superimposing multiple AConvUs to learn the spatial spectral features of different receptive fields of the HSI, reduce the gradient disappearance and enhance the robustness. The proposed multiscale convolution adjusts the shape of the convolution kernel according to the spatial distribution of different superpixels in HSI. Finally, the MSAC classification framework is constructed by the decision fusion of multiscale adaptive convolution at different superpixel scales, which helps to extract the complementary information of the HSI. The MSAC method's experimental performance on several HSI datasets, including the Gaofen-5 (GF-5), to verify the validity and practicality.

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Fusing Multiple Deep Models for In Vivo Human Brain Hyperspectral Image Classification to Identify Glioblastoma Tumor

Cited by 38 publications

References 44 publications

Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms

Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms

Review on the Application of Hyperspectral Imaging Technology of the Exposed Cortex in Cerebral Surgery

Multiscale Adaptive Convolution for Hyperspectral Image Classification

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