Classifier Feature Fusion Using Deep Learning Model for Non-Invasive Detection of Oral Cancer from Hyperspectral Image

Pitchaimani, Jeyaraj; Panigrahi, Bijaya Ketan; Nadar, Edward Rajan Samuel

doi:10.1080/03772063.2020.1786471

Cited by 10 publications

(9 citation statements)

References 22 publications

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“…The majority of these studies used a convolutional neural network (CNN) [ 2 , 15 – 22 , 24 – 26 , 28 , 31 – 36 , 38 – 41 , 43 – 45 , 48 , 49 ]. Several data types such as gene expression data [ 15 , 45 ], spectra data [ 20 , 21 , 29 , 34 , 37 , 44 , 48 ], and other image data types—anatomical [ 16 ], intraoral [ 17 ], histology [ 18 , 27 ], auto-fluorescence [ 19 , 22 ], cytology-image [ 23 ], neoplastic [ 40 ], clinical [ 28 , 36 , 38 ], oral lesions [ 42 ], computed tomography images [ 24 – 26 , 33 , 35 , 41 , 49 ], clinicopathologic [ 2 ], saliva metabolites [ 31 ], histopathological [ 30 , 32 , 43 ], and pathological [ 39 ] images have been used in the included studies.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, specificity and accuracy were also used to demonstrate the performance of the deep learning model for prognostication in OSCC [ 23 ]. Other studies used either accuracy, C-index (concordance index), F1-score, or Dice similarity coefficient (Dsc) mean value as the performance metrics for reporting the potential benefits of the deep learning model [ 2 , 18 , 18 , 26 , 28 , 29 , 31 – 33 , 39 – 41 , 44 , 45 , 49 ].…”

Section: Resultsmentioning

confidence: 99%

“…Most of these studies used either spectra data or computed tomography images [ 20 , 21 , 24 – 26 , 29 , 33 – 35 , 37 , 41 , 44 , 48 , 49 ]. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

Alabi

Bello

Youssef

et al. 2021

Front. Oral. Health

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The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases—PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

Alabi

Bello

Youssef

et al. 2021

Front. Oral. Health

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show abstract

“…For example, for precise diagnosis purposes, deep learning models have been used in the detection of oral cancer [24,25,[64][65][66][67][68][69][70][71][72][73][74][75]. Additionally, these models have assisted in the prediction of lymph node metastasis [27][28][29]76].…”

Section: Deep Learning For Oral Cancer: From Precise Diagnosis To Precision Medicinementioning

confidence: 99%

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

Alabi¹,

Almangush²,

Elmusrati³

et al. 2022

Front. Oral. Health

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Oral squamous cell carcinoma (OSCC) is one of the most prevalent cancers worldwide and its incidence is on the rise in many populations. The high incidence rate, late diagnosis, and improper treatment planning still form a significant concern. Diagnosis at an early-stage is important for better prognosis, treatment, and survival. Despite the recent improvement in the understanding of the molecular mechanisms, late diagnosis and approach toward precision medicine for OSCC patients remain a challenge. To enhance precision medicine, deep machine learning technique has been touted to enhance early detection, and consequently to reduce cancer-specific mortality and morbidity. This technique has been reported to have made a significant progress in data extraction and analysis of vital information in medical imaging in recent years. Therefore, it has the potential to assist in the early-stage detection of oral squamous cell carcinoma. Furthermore, automated image analysis can assist pathologists and clinicians to make an informed decision regarding cancer patients. This article discusses the technical knowledge and algorithms of deep learning for OSCC. It examines the application of deep learning technology in cancer detection, image classification, segmentation and synthesis, and treatment planning. Finally, we discuss how this technique can assist in precision medicine and the future perspective of deep learning technology in oral squamous cell carcinoma.

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“…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 ). Previously, other authors have published comprehensive overviews concerning the application of HSI in gastroenterology ( Ortega et al, 2019 ), wound care ( Saiko et al, 2020 ) or breast cancer therapy and diagnosis ( Aref 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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Classifier Feature Fusion Using Deep Learning Model for Non-Invasive Detection of Oral Cancer from Hyperspectral Image

Cited by 10 publications

References 22 publications

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine

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

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