The Role of AI in “Oral Cancer” Detection: A Systematic Review and Future Perspective

Low survival rates are a major characteristic of “Oral cancer”. Early diagnosis and precise prognosis prediction are critical for appropriate and efficient care of “Oral cancer”. A branch of AI Systems called machine learning has been praised as having the potential to revolutionize cancer care by increasing diagnostic accuracy and prognostication. Despite of this, it has thus far only had a limited impact on patient care or actual medical practice. The study’s objectives include presenting a thorough evaluation of machine learning’s diagnostic and prognostic applications in “Oral cancer” and highlighting some of the drawbacks and issues that physicians face when employing the ML and DL-based models in routine clinical practice. This work given here focuses mostly on the underlying ideas and fundamental vocabulary learning technology of DL and “Oral cancer” detection techniques. The effort then shifts to research based on ML and deep learning that is done on the diagnosis of “Oral cancer”. Finally, the work that has been given offers potential study directions for future deep learning-based research projects for “Oral cancer” diagnosis.

• Publication year

  2023

• Venue

  2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT)

• Publication date

  2023-01-23

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1109/ICSSIT55814.2023.10061090
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Multifractal Texture Analysis of Salivary Fern Pattern for Oral Pre-Cancers and Cancer Assessment

  2021cited by this paper
• Oral cancer detection using optical coherence tomography – A detailed review

  2021cited by this paper
• Machine learning–based MRI texture analysis to predict occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma

  2021cited by this paper
• Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders

  2021cited by this paper
• Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results

  2021cited by this paper
• Algorithm mediated early detection of oral cancer from image analysis.

  2021cited by this paper
• A novel 4-gene signature model simultaneously predicting malignant risk of oral potentially malignant disorders and oral squamous cell carcinoma prognosis.

  2021cited by this paper
• Deep learning neural network for texture feature extraction in oral cancer: enhanced loss function

  2020cited by this paper
• Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer

  2020cited by this paper
• Stratified squamous epithelial biopsy image classifier using machine learning and neighborhood feature selection

  2020cited by this paper
• Diverse spectral band-based deep residual network for tongue squamous cell carcinoma classification using fiber optic Raman spectroscopy.

  2020cited by this paper
• A deep learning algorithm for detection of oral cavity squamous cell carcinoma from photographic images: A retrospective study

  2020cited by this paper
• Immunohistochemical analysis of oral cancer tissue images using support vector machine

  2020cited by this paper
• Current evidence on DNA aneuploidy cytology in noninvasive detection of oral cancer.

  2020cited by this paper
• Application of artificial intelligence and machine learning for prediction of oral cancer risk.

  2020cited by this paper
• Deep Learning-Based Pixel-Wise Lesion Segmentation on Oral Squamous Cell Carcinoma Images

  2020cited by this paper
• Non-Invasive Early Detection of Oral Cancers Using Fluorescence Visualization with Optical Instruments

  2020cited by this paper
• Classifier Feature Fusion Using Deep Learning Model for Non-Invasive Detection of Oral Cancer from Hyperspectral Image

  2020cited by this paper
• Role of PI3K and EGFR in Oral Cancer Progression and Drug Resistance

  2020cited by this paper
• Lipidome-based rapid diagnosis with machine learning for detection of TGF-β signalling activated area in head and neck cancer

  2020cited by this paper
• Facile incorporation of DNA-templated quantum dots for sensitive electrochemical detection of the oral cancer biomarker interleukin-8

  2020cited by this paper
• Automated oral squamous cell carcinoma identification using shape, texture and color features of whole image strips.

  2020cited by this paper
• Identification of diagnostic and prognostic lncRNA biomarkers in oral squamous carcinoma by integrated analysis and machine learning

  2020cited by this paper
• Oral Cancer Analysis Using Machine Learning Techniques

  2019cited by this paper
• Chemometric analysis of integrated FTIR and Raman spectra obtained by non-invasive exfoliative cytology for the screening of oral cancer.

  2019cited by this paper
• Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm

  2019cited by this paper
• FTIR-based spectrum of salivary exosomes coupled with computational-aided discriminating analysis in the diagnosis of oral cancer

  2019cited by this paper
• Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma.

  2019cited by this paper
• Development and Assessment of a Machine Learning Model to Help Predict Survival Among Patients With Oral Squamous Cell Carcinoma.

  2019cited by this paper
• Texture-Map-Based Branch-Collaborative Network for Oral Cancer Detection

  2019cited by this paper
• Tongue squamous cell carcinoma discrimination with Raman spectroscopy and convolutional neural networks

  2019cited by this paper
• Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool

  2019cited by this paper
• Biofunctionalized Nanostructured Yttria Modified Non-Invasive Impedometric Biosensor for Efficient Detection of Oral Cancer

  2019cited by this paper
• Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection

  2019cited by this paper
• Automated detection of oral pre-cancerous tongue lesions using deep learning for early diagnosis of oral cavity cancer

  2019cited by this paper
• Oral Cancer

  2019cited by this paper
• FABnet: feature attention-based network for simultaneous segmentation of microvessels and nerves in routine histology images of oral cancer

  2019cited by this paper
• Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer

  2019cited by this paper
• Cubic CeO2 implanted reduced graphene oxide-based highly sensitive biosensor for non-invasive oral cancer biomarker detection.

  2018cited by this paper
• Enhanced Computational Model for Gravitational Search Optimized Echo State Neural Networks Based Oral Cancer Detection

  2018cited by this paper
• Detecting oropharyngeal carcinoma using multispectral, narrow‐band imaging and machine learning

  2018cited by this paper
• Effective early detection of oral cancer using a simple and inexpensive point of care device in oral rinses

  2018cited by this paper
• Automatic identification of clinically relevant regions from oral tissue histological images for oral squamous cell carcinoma diagnosis.

  2018cited by this paper
• Oral Cancer Early Detection: What Do Patients Need To Know?

  2018cited by this paper
• Textural pattern classification for oral squamous cell carcinoma

  2018cited by this paper
• Anti-IL8/AuNPs-rGO/ITO as an Immunosensing Platform for Noninvasive Electrochemical Detection of Oral Cancer.

  2017cited by this paper
• L-cysteine capped lanthanum hydroxide nanostructures for non-invasive detection of oral cancer biomarker.

  2017cited by this paper
• Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning

  2017cited by this paper
• Detection of oral cancer

  1972cited by this paper

• No citing papers are available for this paper.