Document
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funding4414
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type: BFO_0000020
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type: Grant
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label: CUMex; Spanish Government; Consejo Nacional de Humanidades, Ciencias y Tecnologías, Conahcyt, (254637); Consejo Nacional de Humanidades, Ciencias y Tecnologías, Conahcyt; European Commission, EC, (PID2020‐116417RB‐C42, 865747); European Commission, EC; Agencia Canaria de Investigación, Innovación y Sociedad de la Información, ACIISI, (MCIN/AEI/10.13039/501100011033); Agencia Canaria de Investigación, Innovación y Sociedad de la Información, ACIISI
funding vehicle for: Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks

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Generically Dependent Continuant
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individual
rights: termsOfUse
date: 2026-08-27T06:28:56
publisher: vivo
type: Document
label: RDF description of Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks - http://orbis.uaslp.mx/vivo/individual/scopus11979

Article
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Healthcare Technology Letters
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type: Journal
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type: Periodical
type: Document
publication venue for: Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks
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Information Content Entity
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Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks
type: Continuant
abstract: Hyperspectral imaging has demonstrated its potential to provide correlated spatial and spectral information of a sample by a non-contact and non-invasive technology. In the medical field, especially in histopathology, HSI has been applied for the classification and identification of diseased tissue and for the characterization of its morphological properties. In this work, we propose a hybrid scheme to classify non-tumor and tumor histological brain samples by hyperspectral imaging. The proposed approach is based on the identification of characteristic components in a hyperspectral image by linear unmixing, as a features engineering step, and the subsequent classification by a deep learning approach. For this last step, an ensemble of deep neural networks is evaluated by a cross-validation scheme on an augmented dataset and a transfer learning scheme. The proposed method can classify histological brain samples with an average accuracy of 88%25, and reduced variability, computational cost, and inference times, which presents an advantage over methods in the state-of-the-art. Hence, the work demonstrates the potential of hybrid classification methodologies to achieve robust and reliable results by combining linear unmixing for features extraction and deep learning for classification.
most specific type: Article
page end: 251
related by: authorship_74344
type: Article
freetext keyword: biomedical optical imaging; image classification; learning (artificial intelligence); medical image processing; neural nets Classification (of information); Deep neural networks; Hyperspectral imaging; Medical imaging; Optical data processing; Tumors; Biomedical optical imaging; Brain sample; Brain tumor classifications; HyperSpectral; Images classification; Learning (artificial intelligence); Linear unmixing; Medical images processing; Spatial informations; Spectral information; Article; brain tissue; brain tumor; classifier; convolutional neural network; cross validation; data augmentation; data processing; deep neural network; diagnostic accuracy; feature extraction; histopathology; human; hyperspectral imaging; image analysis; image processing; linear unmixing; sensitivity and specificity; support vector machine; transfer of learning; tumor classification; Image classification
related by: authorship_74339
page start: 240
related by: authorship_74343
type: Document
accesstype: Open Access
has funding vehicle: funding4414
related by: authorship_74345
doi: 10.1049/htl2.12084
most specific type: Document
volume: 11
type: Entity
e iscopus: 2-s2.0-85194492901
has publication venue: Healthcare Technology Letters
related by: authorship_74340
date time value: article date 11980
type: Generically Dependent Continuant
crisid: 11980
type: Information Content Entity
issue: 4
type: Thing
estado: published
related by: authorship_74342