Pre-cancer risk assessment in habitual smokers from DIC images of oral exfoliative cells using active contour and SVM analysis

Dey, Susmita; Sarkar, Ripon; Chatterjee, Kabita; Datta, Pallab; Barui, Ananya; Maity, Santi P.

doi:10.1016/j.tice.2017.01.009

Cited by 24 publications

(22 citation statements)

References 23 publications

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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%

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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%

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

“…SVM classifiers are used to validate the accuracy of classification 24,51‐53 . The Kernel used for the classification is represented as in Equation )

k ((), x, y) = e^{- \frac{{((), ∥ x - y ∥)}^{2}}{2 σ^{2}}}

where σ denotes the kernel width of the Gaussian kernel.…”

Section: Methodsmentioning

confidence: 99%

Automatic segmentation of nuclei from pap smear cell images: A step toward cervical cancer screening

Devaraj¹,

Gnanasaravanan²,

Nirmala³

2020

Int J Imaging Syst Tech

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Cervical cancer is identified as the fourth most recurrent cancer among women across the globe. The cancer is treatable, if identified at the early stage. Pap smear test is the most common and the best tool for initial screening of cancer. Pap smear cell level image analysis is an open issue. The limitation of the analysis is due to the complexity of the cell structure. The smear cell image is composed of cytoplasm and nucleus. The shape and structure of the nucleus determines the cancer prevalence. Segmentation of nucleus is an important step in cancer detection. There are various methods developed for nucleus segmentation. The article proposes multithresholding algorithm to segment cytoplasm and nucleus region from the background. Morphological operations are used for correcting the segmented output. Support vector machine classifier is used for classifying the smear cell as normal or abnormal based on the extracted features of the segmented output. The obtained accuracy of the classifier, sensitivity and specificity for single smear cell are 99.66%, 99.85% and 99.17%.

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“…In [9], the authors use differential interference contrast images of unstained single-cell samples and images of oral epithelial cells stained with Papanicolaou for early detection of cellular anomalies. The active contour snake model is used for the segmentation of DIC images, and the k-means algorithm is used for segmentation in the Papanicolaou images.…”

Section: Computer-assisted Technologies For Diagnosis Of Oral Cancer On Cytology Samples [33]mentioning

confidence: 99%

Segmentation, Detection, and Classification of Cell Nuclei on Oral Cytology Samples Stained with Papanicolaou

et al. 2021

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Although oral cancer is considered a global health issue with 350,000 people diagnosed over a year, it can successfully be treated if diagnosed at early stages. Papanicolaou is an inexpensive and non-invasive method, generally applied to detect cervical cancer, but it can also be useful to detect cancer on oral cavities. The manual process of analyzing cells to detect abnormalities is a time-consuming cell analysis and is subject to variations in perceptions from different professionals. This paper compares three different deep learning (DL) approaches: segmentation, object detection, and image classification. Our results show that the binary object detection with Faster R-CNN is the best approach for nuclei detection and localization (0.76 IoU). Since ResNet 34 had a good performance on abnormal nuclei classification (0.86 F 1 score), we concluded that these two models can be used in combination to perform a reliable localization and classification pipeline. This work reinforces that the automated analysis of oral cytology to build a pipeline for nuclei classification and localization using DL can contribute to minimize the subjectivity of the human analysis and also support the detection of cancer at early stages.

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Pre-cancer risk assessment in habitual smokers from DIC images of oral exfoliative cells using active contour and SVM analysis

Cited by 24 publications

References 23 publications

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

Automatic segmentation of nuclei from pap smear cell images: A step toward cervical cancer screening

Segmentation, Detection, and Classification of Cell Nuclei on Oral Cytology Samples Stained with Papanicolaou

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