<scp>3D‐PulCNN</scp>: Pulmonary cancer classification from hyperspectral images using convolution combination unit based <scp>CNN</scp>

Zhang, Qing; Wang, Yan; Song, Qi; Chen, Jiangang; Li, Sun; Li, Qingli

doi:10.1002/jbio.202100142

Cited by 13 publications

(2 citation statements)

References 41 publications

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“…3D convolutional neural network represents a new tendency in creating such systems with precise outcomes in skin lesion identification. For example, the authors of [118] proposed the Hyper-net, a 3D fully convolutional neural network for more accurate melanoma segmentation from hyperspectral pathology pictures. Hyperspectral pictures are represented by 256 × 256 x 16 cubes as input for Hyper-net.…”

Section: A Single Convolutional Neural Network For Melanoma Classific...mentioning

confidence: 99%

Melanoma Skin Lesion Classification Using Neural Networks: A systematic review

hammo¹,

Younis²

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

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Melanoma is considered a serious health disease and one of the most dangerous and deadly types of skin cancer, due to its unlimited spread. Therefore, detection of this disease must be early and sound due to the high mortality rate. It is driven by researchers' desire to use computers to obtain accurate diagnostic systems to help diagnose and detect this disease early. Given the growing interest in cancer prediction, we have presented this paper, a systematic review of recent developments, using artificial intelligence focusing on melanoma skin lesion detection, particularly systems designed on neural networks. Using the neural networks for melanoma detection could be part of system of assistance for dermatologists who must make the final decision on whether to recommend a biopsy if at least one of the dermatologist's diagnoses and the support system (a helpful method) indicate melanoma or to investigate if another type of cancerous lesion exists. In the latter situation, the system can be trained to recognize distinct types of cancerous skin lesions. On the other hand, the system is incapable of making final decisions. Given neural networks' evolutionary patterns, updated, changed, and integrated networks are expected to increase the performance of such systems. Based on the decision fusion, theoretical and applied contributions were studied using traditional classification algorithms and multiple neural networks. The period 2018-2021 has been focused on new trends. Also for the detection of melanomas, the most popular datasets and how they're being used to train neural network models were presented. Furthermore, the field of research emphasized in order to promote better the subject during different directions. Finally, a research agenda was highlighted to advance the field towards the new trends.

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Section: A Single Convolutional Neural Network For Melanoma Classific...mentioning

confidence: 99%

Melanoma Skin Lesion Classification Using Neural Networks: A systematic review

hammo¹,

Younis²

2022

AL-Rafidain Journal of Computer Sciences and Mathematics

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“…Specifically, HSI measures the intensity changes at multiple wavelengths, demonstrating the reflection, emission or fluorescence interactions with the target tissues, which indicate the changes in the biological structure of its components and changes in the concentration of intrinsic light-absorbing or luminescent chromophores. Researchers have demonstrated the ability of HSI to detect a wide range of diseases, such as oximetry of the retinal ( Gao et al, 2012 ; Hadoux et al, 2019 ; Lim et al, 2021 ), intestinal ischemia identification ( Barberio et al, 2020 ; Mehdorn et al, 2020 ), histopathological tissue analysis ( Khouj et al, 2018 ), detecting cancer metastases in lung and lymph node tissue ( Zhang et al, 2021 ), blood vessel visualization enhancement ( Bjorgan et al, 2015 ; Fouad Aref et al, 2021 ), identifying skin tumors ( Leon et al, 2020 ; Courtenay et al, 2021 ), evaluating the cholesterol levels ( Milanic et al, 2015 ), diabetic foot, etc. In the field of oncology, HSI technology has been successfully applied to detect head and neck cancer ( Halicek et al, 2017 ; Eggert et al, 2022 ), thyroid and salivary glands ( Halicek et al, 2020 ), gastric cancer ( Li et al, 2019 ; Liu et al, 2020a ), oral cancer ( Jeyaraj et al, 2020 ), colon cancer ( Baltussen et al, 2019 ; Manni, 2020 ; Maktabi, 2021 ) as well as breast cancer ( Kho et al, 2019 ; Aboughaleb et al, 2020 ).…”

Section: Introductionmentioning

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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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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3D‐PulCNN: Pulmonary cancer classification from hyperspectral images using convolution combination unit based CNN

Cited by 13 publications

References 41 publications

Melanoma Skin Lesion Classification Using Neural Networks: A systematic review

Melanoma Skin Lesion Classification Using Neural Networks: A systematic review

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

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

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