Hyperspectral Endoscopy Using Deep Learning for Laryngeal Cancer Segmentation

Meyer-Veit, Felix; Rayyes, Rania; Gerstner, Andreas O. H.; Steil, Jochen J.

doi:10.1007/978-3-031-15937-4_57

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…Numerous studies on the classification of larynx (Meyer‐Veit et al, 2022), liver, thyroid, and prostate glandular cancer are carried out using hyperspectral imaging. Maktabi et al, 2020 compare various supervised ML algorithms for tissue discrimination during thyroidectomy (thyroid surgery).…”

Section: Literature Review Of Hyperspectral Imaging In Medical Domainmentioning

confidence: 99%

Hyperspectral imaging for early diagnosis of diseases: A review

et al. 2023

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Hyperspectral Imaging (HSI) has grown to be one of the most crucial optical imaging modalities with applications in numerous industries. The non‐invasive nature of HSI has led to widening its horizon to the medical domain, especially in areas like early diagnosis of various diseases. HSI combines both imaging and spectroscopy properties, thereby exploiting spectral and spatial dimensions of images captured, providing quick and accurate interpretation of data. The current study aims to give an exhaustive overview of HSI's applications pertaining to the medical industry for fast detection of diseases and aiding in surgical procedures. The survey focuses on hyperspectral imaging combined with various approaches ‐ machine learning, deep learning, genetic algorithms, and anomaly detection for the treatment of disorders. In addition, the survey highlights accompanying pre‐processing approaches, performance metrics, inferences, and future prospects of HSI in the medical domain. The current study can gauge computer vision specialists, researchers in machine and deep learning domain, doctors, and scientists by giving them a platform for improving existing treatment methods for the betterment of society.

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Section: Literature Review Of Hyperspectral Imaging In Medical Domainmentioning

confidence: 99%

Hyperspectral imaging for early diagnosis of diseases: A review

et al. 2023

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“…In Meyer-Veit et al [ 13 ], an effective HIs-DL technique was projected for predicting LCA. Primarily, an important wavelength analysis was accomplished for identifying the highly useful channels in the HS cubes for decreasing the noise as well as increasing the prediction.…”

Section: Literature Workmentioning

confidence: 99%

Automated Laryngeal Cancer Detection and Classification Using Dwarf Mongoose Optimization Algorithm with Deep Learning

Mohamed,

Almutairi,

Abdelrahim

et al. 2023

Cancers

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Laryngeal cancer (LCA) is a serious disease with a concerning global rise in incidence. Accurate treatment for LCA is particularly challenging in later stages, due to its complex nature as a head and neck malignancy. To address this challenge, researchers have been actively developing various analysis methods and tools to assist medical professionals in efficient LCA identification. However, existing tools and methods often suffer from various limitations, including low accuracy in early-stage LCA detection, high computational complexity, and lengthy patient screening times. With this motivation, this study presents an Automated Laryngeal Cancer Detection and Classification using a Dwarf Mongoose Optimization Algorithm with Deep Learning (ALCAD-DMODL) technique. The main objective of the ALCAD-DMODL method is to recognize the existence of LCA using the DL model. In the presented ALCAD-DMODL technique, a median filtering (MF)-based noise removal process takes place to get rid of the noise. Additionally, the ALCAD-DMODL technique involves the EfficientNet-B0 model for deriving feature vectors from the pre-processed images. For optimal hyperparameter tuning of the EfficientNet-B0 model, the DMO algorithm can be applied to select the parameters. Finally, the multi-head bidirectional gated recurrent unit (MBGRU) model is applied for the recognition and classification of LCA. The simulation result analysis of the ALCAD-DMODL technique is carried out on the throat region image dataset. The comparison study stated the supremacy of the ALCAD-DMODL technique in terms of distinct measures.

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“…In all approaches, the cost of the training and test time is still high which is a considerable challenge for medical applications like the one considered here, where ideally the medical practitioner should see the result in vivo immediately during the endoscopic examination of the larynx. In our previous work [9], we proposed In this paper, we investigate how to get information-rich images from HS imaging as well as how to determine the most relevant information in the captured images to speed up the prediction and training process. It turns out that several factors are crucial including the spectral resolution of the captured images, the time of exposure to the light source during image acquisition, and the wavelength range that carries the most relevant information.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Wavelength Analysis with U-Net for Larynx Cancer Detection

Meyer-Veit¹,

Rayyes²,

Gerstner³

et al. 2022

ESANN 2022 Proceedings

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Early detection of laryngeal tumors is critical for their successful therapy. In this paper, we investigate how hyperspectral (HS) imaging can contribute to this aim based on an in-vivo data set of 13 HS image cubes recorded in clinical practice. We perform semantic segmentation with a tailored U-Net trained on labels provided by the clinicians. We specifically investigate the influence of exposure time during image acquisition, the suitable wavelengths to determine the most informative image channels, and present quantitative results on accuracy and the AUC measure.* The data set collection was funded by the German Cancer Aid within the project framework "Early detection of laryngeal cancer by means of Hyperspectral Imaging (109825110275)".

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Hyperspectral Endoscopy Using Deep Learning for Laryngeal Cancer Segmentation

Cited by 5 publications

References 20 publications

Hyperspectral imaging for early diagnosis of diseases: A review

Hyperspectral imaging for early diagnosis of diseases: A review

Automated Laryngeal Cancer Detection and Classification Using Dwarf Mongoose Optimization Algorithm with Deep Learning

Hyperspectral Wavelength Analysis with U-Net for Larynx Cancer Detection

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