Tissue Level Based Deep Learning Framework for Early Detection of
                    Dysplasia in Oral Squamous Epithelium

Gupta, Rachit; Kaur, Mandeep; Manhas, Jatinder

doi:10.33851/jmis.2019.6.2.81

Cited by 38 publications

(30 citation statements)

References 21 publications

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“…Methods related to the automated diagnosis of oral cancer, OPMDs and benign lesions are largely based on microscopic images [9]- [12]. Other literature covers the use of multidimensional hyperspectral images of the mouth [13], the use of CT (computed tomography) images [14], the use of autofluorescence [15], [16] and fluorescence imaging [17] which focused on relative close-ups of the oral lesions and, finally, standard white light images which captured oral cavity structures [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer

et al. 2020

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Oral cancer is a major global health issue accounting for 177,384 deaths in 2018 and it is most prevalent in low-and middle-income countries. Enabling automation in the identification of potentially malignant and malignant lesions in the oral cavity would potentially lead to low-cost and early diagnosis of the disease. Building a large library of well-annotated oral lesions is key. As part of the MeMoSA ® (Mobile Mouth Screening Anywhere) project, images are currently in the process of being gathered from clinical experts from across the world, who have been provided with an annotation tool to produce rich labels. A novel strategy to combine bounding box annotations from multiple clinicians is provided in this paper. Further to this, deep neural networks were used to build automated systems, in which complex patterns were derived for tackling this difficult task. Using the initial data gathered in this study, two deep learning based computer vision approaches were assessed for the automated detection and classification of oral lesions for the early detection of oral cancer, these were image classification with ResNet-101 and object detection with the Faster R-CNN. Image classification achieved an F1 score of 87.07% for identification of images that contained lesions and 78.30% for the identification of images that required referral. Object detection achieved an F1 score of 41.18% for the detection of lesions that required referral. Further performances are reported with respect to classifying according to the type of referral decision. Our initial results demonstrate deep learning has the potential to tackle this challenging task. INDEX TERMS Composite annotation, deep learning, image classification, object detection, oral cancer, oral potentially malignant disorders.

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Section: Introductionmentioning

confidence: 99%

Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer

et al. 2020

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“…To classify patches as normal or suspicious, we used deep CNN, which has been successfully applied in image classification, segmentation, object detection, video processing, natural language processing, and speech recognition [ 25 , 26 ]. For oral cancer prognosis or diagnosis, CNN has been applied to histological images [ 9 , 27 , 28 , 29 ] and on autofluorescence and white light images [ 18 , 19 ]. To our knowledge, this is the first application of CNN for the classification of oral lesions from clinical photographic images.…”

Section: Methodsmentioning

confidence: 99%

“…Santos et al presented a method for automated nuclei segmentation on dysplastic oral tissues from histological images using CNN [ 7 ] with 86% sensitivity and 89% specificity. Another CNN-based study proposed a framework for the classification of dysplastic tissue images to four different classes with 91.65% training and 89.3% testing accuracy using transfer learning [ 8 , 9 ]. Yet another CNN-based transfer learning approach study proposed by Das et al [ 10 ] also classified the multi-class grading for diagnosing patients with OSCC.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results

Çamalan

Mahmood

Binol

et al. 2021

Cancers

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Oral cancer/oral squamous cell carcinoma is among the top ten most common cancers globally, with over 500,000 new cases and 350,000 associated deaths every year worldwide. There is a critical need for objective, novel technologies that facilitate early, accurate diagnosis. For this purpose, we have developed a method to classify images as “suspicious” and “normal” by performing transfer learning on Inception-ResNet-V2 and generated automated heat maps to highlight the region of the images most likely to be involved in decision making. We have tested the developed method’s feasibility on two independent datasets of clinical photographic images of 30 and 24 patients from the UK and Brazil, respectively. Both 10-fold cross-validation and leave-one-patient-out validation methods were performed to test the system, achieving accuracies of 73.6% (±19%) and 90.9% (±12%), F1-scores of 97.9% and 87.2%, and precision values of 95.4% and 99.3% at recall values of 100.0% and 81.1% on these two respective cohorts. This study presents several novel findings and approaches, namely the development and validation of our methods on two datasets collected in different countries showing that using patches instead of the whole lesion image leads to better performance and analyzing which regions of the images are predictive of the classes using class activation map analysis.

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“…Marc Aubreville et.al, [18] have analysed confocal laser endomicroscopic images to diagnose oral squamous cell carcinoma using deep learning techniques. Gupta et.al, [19] have made use of a deep architecture for diagnosis of dysplasia in microscopic images. Orofacial disease classification using deep learning architectures have also been attempted by the authors of [20].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Deep Neural Network Approach for Oral Cancer Screening

Nanditha¹,

A²,

Chandrashekar³

et al. 2021

Int. J. Onl. Eng.

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<p class="0abstract"><span lang="EN-US">One of the ways to reduce oral cancer mortality rate is diagnosing oral lesions at initial stages to classify them as precancerous or normal lesions. During routine oral examination, oral lesions are normally screened manually. In a low resource setting area where there is lack of medical facilities and also medical expertise, an automated mechanism for oral cancer screening is required. The present work is an attempt towards developing an automated system for diagnosing oral lesions using deep learning techniques. An ensemble deep learning model that combines the benefits of Resnet-50 and VGG-16 has been developed. This model has been trained with an augmented dataset of oral lesion images. The model outperforms other popularly used deep learning models in performing the classification of oral images. An accuracy of 96.2%, 98.14% sensitivity and 94.23% specificity was achieved with the ensemble deep learning model.</span></p>

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Tissue Level Based Deep Learning Framework for Early Detection of Dysplasia in Oral Squamous Epithelium

Cited by 38 publications

References 21 publications

Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer

Automated Detection and Classification of Oral Lesions Using Deep Learning for Early Detection of Oral Cancer

Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results

An Ensemble Deep Neural Network Approach for Oral Cancer Screening

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