An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks

Xu, Shipu; Liu, Yong; Hu, Wenwen; Zhang, Chenxi; Chang, Liu; Zong, Yongshuo; Chen, Sirui; Lu, Yiwen; Yang, Longzhi; Ng, E. Y. K.; Wang, Yongtong; Wang, Yunsheng

doi:10.1109/access.2019.2950286

Cited by 55 publications

(30 citation statements)

References 18 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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“…In order to detect oral cancer in the early stages, Xu et al established a three-dimensional CNN algorithm. Their results show that 3DCNNs outperform 2DCNNs in identifying benign and malignant lesions [ 27 ]. Welikala et al presented the automated detection and classification of oral cancer in the early stages.…”

Section: Introductionmentioning

confidence: 99%

“…According to the presented studies different AI approaches have proven successful for clinical analysis [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. However, in the terms of histological analysis of OC, fewer studies have been conducted since histopathological examination is highly invasive [ 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue

Musulin

Štifanić

Zulijani

et al. 2021

Cancers

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Oral squamous cell carcinoma is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. For this reason, artificial intelligence (AI) algorithms are widely used as computational aid in the diagnosis for classification and segmentation of tumors, in order to reduce inter- and intra-observer variability. In this research, a two-stage AI-based system for automatic multiclass grading (the first stage) and segmentation of the epithelial and stromal tissue (the second stage) from oral histopathological images is proposed in order to assist the clinician in oral squamous cell carcinoma diagnosis. The integration of Xception and SWT resulted in the highest classification value of 0.963 (σ = 0.042) AUCmacro and 0.966 (σ = 0.027) AUCmicro while using DeepLabv3+ along with Xception_65 as backbone and data preprocessing, semantic segmentation prediction resulted in 0.878 (σ = 0.027) mIOU and 0.955 (σ = 0.014) F1 score. Obtained results reveal that the proposed AI-based system has great potential in the diagnosis of OSCC.

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“…The size of the convolution kernel selected in the two convolutional layers is 5×5×2 in three dimensions [29] and 3×2 in two dimensions, with a setting number of 64 and 128 convolution kernels respectively. In both the pooling layers, the kernel size [30,31] selected is the same as 2 × 2, and the function is the max pooling function. Therefore, the size of convolution layers is 12 × 18 (step size 5, no padding) and 4 × 8 (step size 1, no padding).…”

Section: B Convolutional Neural Networkmentioning

confidence: 99%

The Connectivity Evaluation Among Wells in Reservoir Utilizing Machine Learning Methods

Wang

Wei

et al. 2020

IEEE Access

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Machine learning is becoming prevalent increasingly for reservoir characteristics analysis in the petroleum industry. This investigation proposes an alternative way for evaluating interwell connectivity in oil fields utilizing machine learning. In this study, three-dimensional convolutional neural network (CNN) was utilized to establish a deep learning model, which can invert interwell connectivity combining with dynamic production data. Different from traditional methods that try to construct mathematical formulas to calculate the connectivity among wells basing on physical laws, deep learning model can capture autonomously the changing characteristics of dynamic production data by training continuously and provide a potential to characterize the interwell connectivity accurately without physical model. At the same time, the back propagation (BP) neural network has also been built to analyze the prediction performance, which are compared with CNN. The results demonstrate that CNN has better performance in predicting the connectivity with the overall AARD below 15.35%. Moreover, the connectivity predicted by CNN is closest to the real connectivity factor compared with some traditional methods. The evaluation method on interwell connectivity proposed by this paper provides effective guidance for the secondary development of both conventional and unconventional reservoirs. INDEX TERMS BP neural network, convolutional neural network, dynamic production data, interwell connectivity, machine learning.

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An Early Diagnosis of Oral Cancer based on Three-Dimensional Convolutional Neural Networks

Cited by 55 publications

References 18 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

An Enhanced Histopathology Analysis: An AI-Based System for Multiclass Grading of Oral Squamous Cell Carcinoma and Segmenting of Epithelial and Stromal Tissue

The Connectivity Evaluation Among Wells in Reservoir Utilizing Machine Learning Methods

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