Oral cancer detection using data mining tool

Tetarbe, Arushi; Choudhury, Tanupriya; Toe, Teoh Teik; Rawat, Seema

doi:10.1109/icatcct.2017.8389103

Cited by 7 publications

(10 citation statements)

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“…A total of 384 publications were found to be duplicates and were discarded, thus leaving 1167 articles of which 1110 were excluded after evaluation of the title and abstract. In the second step, and after full-text reading of the 62 selected articles, a total of 36 were included in the review [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 ], with a description of the relevant findings ( Figure 1 ).…”

Section: Resultsmentioning

confidence: 99%

“…Only five of the selected articles were published before 2015 [ 33 , 38 , 44 , 58 , 59 ]. The field work of 12 studies was carried out in India [ 29 , 30 , 34 , 39 , 44 , 49 , 51 , 52 , 54 , 55 , 56 , 57 ], while five studies were carried out in Malaysia [ 37 , 42 , 47 , 48 , 61 ] and in China [ 28 , 35 , 43 , 59 , 60 ], four in the United States [ 36 , 38 , 45 , 46 ], two in The Netherlands [ 33 , 58 ] and Poland [ 40 , 41 ], and one in Taiwan [ 32 ], Saudi Arabia [ 50 ], Morocco [ 31 ], Germany [ 27 ], Greece [ 53 ] and Sweden [ 62 ].…”

Section: Resultsmentioning

confidence: 99%

“…The study subjects and aims were classified as follows: (1) identification of the most appropriate biopsy site [ 28 ]; (2) selection of patients through clinical screening and the referral of suspicious cases to a specialist in oral medicine or oral cancer [ 30 , 37 , 52 , 57 , 61 ]; (3) screening in smokers [ 34 , 49 ]; (4) oral cancer screening through smart telecytology [ 54 ]; (5) the diagnosis of OPMDs such as solar cheilosis [ 53 ], oral lichen planus [ 41 , 46 ], leukoplakia [ 29 , 40 , 58 ], with a prediction of their course [ 43 ]; (6) aids to differential diagnosis by classifying the lesions as benign or precancerous [ 50 ]; normal mucosa or oral cancer [ 27 , 32 , 35 , 36 , 39 , 42 , 62 ]; or as different benign, premalignant and malignant lesions [ 31 , 33 , 39 , 46 , 59 ]; (7) classification of oral cancer [ 56 ]; and (8) development of oral cancer risk predictive models [ 42 , 47 , 48 , 51 , 55 , 60 ].…”

Section: Resultsmentioning

confidence: 99%

“…(5) the diagnosis of OPMDs such as solar cheilosis [53], oral lichen planus [41,46], leukoplakia [29,40,58], with a prediction of their course [43]; (6) aids to differential diagnosis by classifying the lesions as benign or precancerous [50]; normal mucosa or oral cancer [27,32,35,36,39,42,62]; or as different benign, premalignant and malignant lesions [31,33,39,46,59]; (7) classification of oral cancer [56]; and (8) development of oral cancer risk predictive models [42,47,48,51,55,60].…”

Section: Description Of Studiesmentioning

confidence: 99%

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Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

García-Pola

Pons-Fuster

Suárez-Fernández

et al. 2021

Cancers

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The early diagnosis of cancer can facilitate subsequent clinical patient management. Artificial intelligence (AI) has been found to be promising for improving the diagnostic process. The aim of the present study is to increase the evidence on the application of AI to the early diagnosis of oral cancer through a scoping review. A search was performed in the PubMed, Web of Science, Embase and Google Scholar databases during the period from January 2000 to December 2020, referring to the early non-invasive diagnosis of oral cancer based on AI applied to screening. Only accessible full-text articles were considered. Thirty-six studies were included on the early detection of oral cancer based on images (photographs (optical imaging and enhancement technology) and cytology) with the application of AI models. These studies were characterized by their heterogeneous nature. Each publication involved a different algorithm with potential training data bias and few comparative data for AI interpretation. Artificial intelligence may play an important role in precisely predicting the development of oral cancer, though several methodological issues need to be addressed in parallel to the advances in AI techniques, in order to allow large-scale transfer of the latter to population-based detection protocols.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Description Of Studiesmentioning

confidence: 99%

See 2 more Smart Citations

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

García-Pola

Pons-Fuster

Suárez-Fernández

et al. 2021

Cancers

View full text Add to dashboard Cite

show abstract

“…Fractal analysis on time‐resolved autofluorescence measurement in tumors and healthy tissues of the oral cavity ex vivo have been shown too 14 . Studies also deal with developing classifiers for classification of oral cancer based on clinical values of chosen factors from patient records 15‐17 . Analyzing clinically significant and biologically interpretable features from microscopic biopsy images, a framework for automated detection and classification of cancer is proposed and examined 18 .…”

Section: Introductionmentioning

confidence: 99%

Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques

et al. 2020

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Background Oral squamous cell carcinoma (OSCC) is the most prevalent form of oral cancer. Very few researches have been carried out for the automatic diagnosis of OSCC using artificial intelligence techniques. Though biopsy is the ultimate test for cancer diagnosis, analyzing a biopsy report is a very much challenging task. To develop computer‐assisted software that will diagnose cancerous cells automatically is very important and also a major need of the hour. Aim To identify OSCC based on morphological and textural features of hand‐cropped cell nuclei by traditional machine learning methods. Methods In this study, a structure for semi‐automated detection and classification of oral cancer from microscopic biopsy images of OSCC, using clinically significant and biologically interpretable morphological and textural features, are examined and proposed. Forty biopsy slides were used for the study from which a total of 452 hand‐cropped cell nuclei has been considered for morphological and textural feature extraction and further analysis. After making a comparative analysis of commonly used methods in the segmentation technique, a combined technique is proposed. Our proposed methodology achieves the best segmentation of the nuclei. Henceforth the features extracted were fed into five classifiers, support vector machine, logistic regression, linear discriminant, k‐nearest neighbors and decision tree classifier. Classifiers were also analyzed by training time. Another contribution of the study is a large indigenous cell level dataset of OSCC biopsy images. Results We achieved 99.78% accuracy applying decision tree classifier in classifying OSCC using morphological and textural features. Conclusion It is found that both morphological and textural features play a very important role in OSCC diagnosis. It is hoped that this type of framework will help the clinicians/pathologists in OSCC diagnosis.

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A systematic review for detecting cancer using machine learning techniques

Sharma¹,

Prabha²

2022

Innovations in Computational and Computer Techniques: Icacct-2021

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Oral cancer detection using data mining tool

Cited by 7 publications

References 1 publication

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review

Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques

A systematic review for detecting cancer using machine learning techniques

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