Diverse spectral band-based deep residual network for tongue squamous cell carcinoma classification using fiber optic Raman spectroscopy

Ding, Jingya; Yu, Mingxin; Zhu, Lianqing; Zhang, Tao; Xia, Jiabin; Sun, Greg

doi:10.1016/j.pdpdt.2020.102048

Cited by 20 publications

(16 citation statements)

References 28 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%

“…A single study reported the performance of deep learning with four different performance metrics (sensitivity, specificity, accuracy, and area under receiving operating characteristics curve [AUC]) [ 16 ]. Similarly, a total of 11 studies reported the combination of the trio of sensitivity, specificity, and accuracy as the performance metrics for the deep machine learning method [ 15 , 19 – 21 , 24 , 25 , 30 , 35 , 37 , 38 , 42 ]. Both specificity and sensitivity were used to depict the performance of the model [ 17 , 20 , 22 , 27 , 48 ].…”

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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“…In this paper, the YOLOv3 Network is used as the prototype, and the residual network (ResNet) [20,21] is used to skip the layer connection mode to increase the network depth and still make the network convergence, which achieves end-to-end target detection and identification. YOLOv3 neural network can predict the object and its position information in the image only by looking at the image once.…”

Section: Yolov3 Networkmentioning

confidence: 99%

A novel one-stage object detection network for multi-scene vehicle attribute recognition

Zhang¹

2018

ICST Transactions on Scalable Information Systems

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Diverse spectral band-based deep residual network for tongue squamous cell carcinoma classification using fiber optic Raman spectroscopy

Cited by 20 publications

References 28 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

A novel one-stage object detection network for multi-scene vehicle attribute recognition

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