Hyperspectral Wavelength Analysis with U-Net for Larynx Cancer Detection

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

doi:10.14428/esann/2022.es2022-100

Cited by 5 publications

(2 citation statements)

References 10 publications

(14 reference statements)

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“…The early stage of LC and pre-malignant is related to a greater degree of laryngeal protection together with a clear diagnosis in actual time, whereas extreme levels of LC require a multimodal diagnostic strategy that leads to poor life quality and crucial toxicities [3]. As regards an enhanced diagnostic strategy, current research determines a high rate of frequency with total survival of 34 to 62% [4]. The initial identification and accurate diagnosis of cancer are crucial for achieving great medical results [5].…”

Section: Introductionmentioning

confidence: 99%

Laryngeal Cancer Detection and Classification Using Aquila Optimization Algorithm With Deep Learning on Throat Region Images

Alrowais,

Mahmood,

Alotaibi

et al. 2023

IEEE Access

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Laryngeal cancer detection on throat area images is a vital application of medical image diagnosis and computer vision (CV) in the healthcare domain. It contains the analysis and detection of cancerous or abnormal tissues from the larynx, an essential part of the respiratory and vocal systems. Several machine learning (ML) and deep learning (DL) systems are executed for classifying the extraction features as both cancerous and healthy tissue. Convolutional Neural Networks (CNNs) and recurrent neural networks (RNNs) have shown promise in this context. With this motivation, this study designs a new Laryngeal Cancer Detection and Classification using the Aquila Optimization Algorithm with Deep Learning (LCDC-AOADL) technique on neck region images. The purpose of the LCDC-AOADL technique is to examine the histopathological images for the recognition and classification of Laryngeal Cancer. In the presented LCDC-AOADL technique, the Inceptionv3 model is used for the feature extraction process. Besides, the LCDC-AOADL technique employed a deep belief network (DBN) model for the identification and classification of LC. Moreover, the AOA is utilized for the hyperparameter tuning of the DBN model which results in improved detection rate. The simulation analysis of the LCDC-AOADL method is validated on the benchmark Laryngeal dataset. The experimental results pointed out the enhanced detection results of the LCDC-AOADL technique over other recent approaches with a maximum accuracy of 96.02%, precision of 92.10%, recall of 91.87%, and F-score of 91.86%.

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Section: Introductionmentioning

confidence: 99%

Laryngeal Cancer Detection and Classification Using Aquila Optimization Algorithm With Deep Learning on Throat Region Images

Alrowais,

Mahmood,

Alotaibi

et al. 2023

IEEE Access

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“…It has a model that must overcome classification and identification issues. By comparison with standard image processing techniques, CNN has a higher ability for evaluation and feature extraction [ 9 ]. Presently, artificial intelligence (AI) depends on deep CNNs (DCNNs) that could be implemented in pathology, magnetic resonance images (MRIs), classification of skin cancer, congenital cataracts, and diabetic retinopathy (DR) analysis [ 10 ].…”

Section: Introductionmentioning

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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