Burn characterization using object‐oriented hyperspectral image classification

Parasca, Sorin Viorel

doi:10.1002/jbio.202200106

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…The classification effectiveness of a classifier before and after transfer learning between samples was measured using overall accuracy, sensitivity and specificity [51]. Overall accuracy is one of the most commonly used metrics to assess the performance of classification models.…”

Section: Methodsmentioning

confidence: 99%

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning

Zhang,

Xue,

Xie

et al. 2023

Journal of Biophotonics

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Acquiring large amounts of hyperspectral data of small intestinal tissue with real labels in the clinic is difficult, and the data shows inter‐patient variability. Building an automatic identification model using a small dataset presents a crucial challenge in obtaining a strong generalization of the model. This study aimed to explore the performance of hyperspectral imaging and transfer learning techniques in the automatic identification of normal and ischemic necrotic sites in small intestinal tissue. Hyperspectral data of small intestinal tissues were collected from eight white rabbit samples. The transfer component analysis (TCA) method was performed to transfer learning on hyperspectral data between different samples and the variability of data distribution between samples was reduced. The results showed that the TCA transfer learning method improved the accuracy of the classification model with less training data. This study provided a reliable method for single‐sample modelling to detect necrotic sites in small intestinal tissue .

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Section: Methodsmentioning

confidence: 99%

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning

Zhang,

Xue,

Xie

et al. 2023

Journal of Biophotonics

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“…In recent years, machine learning techniques have made marked advances in the field of HSI [32]. In the biomedical field, most studies reports on the application of HSI combined with supervised classification [33]. However, collecting a sufficient number of labeled training samples in certain clinical procedures is typically expensive, and some are not possible.…”

Section: Introductionmentioning

confidence: 99%

Discrimination between normal and necrotic small intestinal tissue using hyperspectral imaging and unsupervised classification

Zhang

Huang

Chen

et al. 2023

Journal of Biophotonics

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Objective and automatic clinical discrimination of normal and necrotic sites of small intestinal tissue remains challenging. In this study, hyperspectral imaging (HSI) and unsupervised classification techniques were used to distinguish normal and necrotic sites of small intestinal tissues. Small intestinal tissue hyperspectral images of eight Japanese large‐eared white rabbits were acquired using a visible near‐infrared hyperspectral camera, and K‐means and density peaks (DP) clustering algorithms were used to differentiate between normal and necrotic tissue. The three cases in this study showed that the average clustering purity of the DP clustering algorithm reached 92.07% when the two band combinations of 500–622 and 700–858 nm were selected. The results of this study suggest that HSI and DP clustering can assist physicians in distinguishing between normal and necrotic sites in the small intestine in vivo.

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BOISO: Weight optimized U-Net architecture for segmentation of hyperspectral image

Bhuvaneshwarri,

Stateczny,

Kokku

et al. 2024

Preprint

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Recently, the Hyper Spectral Image (HSI) classification relies as a well-established study area in the topic related to Remote Sensing (RS). The classification of HSI is used in various applications such as military, agriculture, mineral mapping and so on. However, the existing techniques have underlying difficulties related to curse of dimensionalities and the lack of training data. To overcome these issues, this research using Butterfly Optimization Integrated Snake Optimization (BOISO) optimized U-Net for segmenting HSI. After data acquisition from Indian Pines dataset, the pre-processing is done using Weiner filter. Next to this, the proposed BOISO optimized U-Net is used to segment the pre-processed HSI. The spatial feature weight map is obtained based on spatial information path and corresponding features are obtained by multiplying semantic feature map. Finally, the feature map is linked with the spatial location to obtain final feature map which is optimized using the proposed BOISO. Then, the classification using hybrid classification approach is based on geometric mean of improved Deep Belief network (DBN) and Quantum Neural Network (QNN). The results exhibit that the BOISO achieves a sensitivity of 0.939 which is higher than SO, BOA, BES and PRO, with respective sensitivities of 0.877, 0.893, 0.847 and 0.857.

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Burn characterization using object‐oriented hyperspectral image classification

Cited by 3 publications

References 28 publications

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning

Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning

Discrimination between normal and necrotic small intestinal tissue using hyperspectral imaging and unsupervised classification

BOISO: Weight optimized U-Net architecture for segmentation of hyperspectral image

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