Oro Vision: Deep Learning for Classifying Orofacial Diseases

Anantharaman, R; Anantharaman, Vidya; Lee, Yugyung

doi:10.1109/ichi.2017.69

Cited by 24 publications

(13 citation statements)

References 8 publications

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“…The scope of the studies in the related literature have mostly been limited to certain types of oral lesions, such as mouth sores [ 17 , 18 ] or tongue lesions [ 19 ], which represent only a small fraction of the oral lesions. In a more recent study by Welikala et al, the authors investigated the feasibility of deep learning methods for detection and classification of oral lesions based on referral decisions using a more comprehensive dataset [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

“…The literature on image-based automated diagnosis of oral cancer has largely focused on the use of special imaging technologies, such as optical coherence tomography [ 8 , 9 ], hyperspectral imaging [ 10 ], and autofluorescence imaging [ 11 , 12 , 13 , 14 , 15 , 16 ]. On the other hand, there have been a handful of studies performed with white-light photographic images [ 17 , 18 , 19 , 20 , 21 ], most of which focus on the identification of certain types of oral lesions.…”

Section: Introductionmentioning

confidence: 99%

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Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders

Tanriver

Tekkeşın

Ergen

2021

Cancers

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Oral cancer is the most common type of head and neck cancer worldwide, leading to approximately 177,757 deaths every year. When identified at early stages, oral cancers can achieve survival rates of up to 75–90%. However, the majority of the cases are diagnosed at an advanced stage mainly due to the lack of public awareness about oral cancer signs and the delays in referrals to oral cancer specialists. As early detection and treatment remain to be the most effective measures in improving oral cancer outcomes, the development of vision-based adjunctive technologies that can detect oral potentially malignant disorders (OPMDs), which carry a risk of cancer development, present significant opportunities for the oral cancer screening process. In this study, we explored the potential applications of computer vision techniques in the oral cancer domain within the scope of photographic images and investigated the prospects of an automated system for detecting OPMD. Exploiting the advancements in deep learning, a two-stage model was proposed to detect oral lesions with a detector network and classify the detected region into three categories (benign, OPMD, carcinoma) with a second-stage classifier network. Our preliminary results demonstrate the feasibility of deep learning-based approaches for the automated detection and classification of oral lesions in real time. The proposed model offers great potential as a low-cost and non-invasive tool that can support screening processes and improve detection of OPMD.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders

Tanriver

Tekkeşın

Ergen

2021

Cancers

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“…This is a hallmark of enzymatic catalysis, where diversity of function on common catalyst scaffolds has emerged alongside an impressive array of catalyst specificities. 32…”

Section: Discussionmentioning

confidence: 99%

Disparate Catalytic Scaffolds for Atroposelective Cyclodehydration

et al. 2019

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Catalysts that control stereochemistry are prized tools in chemical synthesis. When an effective catalyst is found, it is often explored for other types of reactions, frequently under the auspices of different mechanisms. As successes mount, a unique catalyst scaffold may become viewed as "privileged". However, the mechanistic hallmarks of privileged catalysts are not easily enumerated, nor readily generalized to genuinely different classes of reactions or substrates. We explored the concept of scaffold uniqueness with two catalyst types for an unusual atropisomerselective cyclodehydration: (a) C 2-symmetric chiral phosphoric acids, and (b) phosphothreonineembedded, peptidic phosphoric acids. Pragmatically, both catalyst scaffolds proved fertile for enantioselective/atroposelective cyclodehydrations. Mechanistic studies revealed that the determinants of often equivalent and high atroposelectivity are different for the two catalyst classes. A data-descriptive classification of these asymmetric catalysts reveals an increasingly broad set of catalyst chemotypes, operating with different mechanistic features, that creates new opportunities for broad and complementary application of catalyst scaffolds in diverse substrate space.

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“…Early publications in the field focused on texture based features, Thomas [18] used the grey level co-occurrence matrix and grey level run-length, whilst Krishnan [9] made use of higher order spectra, local binary pattern and laws texture energy. The more recent papers [10]- [17], [19], [20] have made the shift towards employing deep learning, which are artificial neural networks that consist of many layers of neurons and rely on large datasets and fast computing power to enable them to learn complex patterns. More specifically these publications made use of the deep convolutional neural network (CNN) whose architectures made the explicit assumption that the inputs were in the form of images.…”

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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Oro Vision: Deep Learning for Classifying Orofacial Diseases

Cited by 24 publications

References 8 publications

Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders

Automated Detection and Classification of Oral Lesions Using Deep Learning to Detect Oral Potentially Malignant Disorders

Disparate Catalytic Scaffolds for Atroposelective Cyclodehydration

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

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