Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network

Das, Navarun; Hussain, Elima; Mahanta, Lipi B.

doi:10.1016/j.neunet.2020.05.003

Cited by 127 publications

(77 citation statements)

References 21 publications

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“…Talo et al (15) demonstrated that the ResNet-50 model achieved the best classification accuracy while the Alexnet model obtained the lowest performance among Alexnet, ResNet-18, ResNet-34, and ResNet-50 pretrained models in five classes of brain abnormality classification MR images. In another study (25), the ResNet-50 model also achieved the highest classification accuracy among four pretrained models in automating four classes of oral squamous cell carcinoma.…”

Section: Discussionmentioning

confidence: 94%

Differentiation of Glioma Mimicking Encephalitis and Encephalitis Using Multiparametric MR-Based Deep Learning

et al. 2021

Front. Oncol.

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BackgroundComputational aid for diagnosis based on convolutional neural network (CNN) is promising to improve clinical diagnostic performance. Therefore, we applied pretrained CNN models in multiparametric magnetic resonance (MR) images to classify glioma mimicking encephalitis and encephalitis.MethodsA data set containing 3064 MRI brain images from 164 patients with a final diagnosis of glioma (n = 56) and encephalitis (n = 108) patients and divided into training and testing sets. We applied three MRI modalities [fluid attenuated inversion recovery (FLAIR), contrast enhanced-T1 weighted imaging (CE-T1WI) and T2 weighted imaging (T2WI)] as the input data to build three pretrained deep CNN models (Alexnet, ResNet-50, and Inception-v3), and then compared their classification performance with radiologists’ diagnostic performance. These models were evaluated by using the area under the receiver operator characteristic curve (AUC) of a five-fold cross-validation and the accuracy, sensitivity, specificity were analyzed.ResultsThe three pretrained CNN models all had AUC values over 0.9 with excellent performance. The highest classification accuracy of 97.57% was achieved by the Inception-v3 model based on the T2WI data. In addition, Inception-v3 performed statistically significantly better than the Alexnet architecture (p<0.05). For Inception-v3 and ResNet-50 models, T2WI offered the highest accuracy, followed by CE-T1WI and FLAIR. The performance of Inception-v3 and ResNet-50 had a significant difference with radiologists (p<0.05), but there was no significant difference between the results of the Alexnet and those of a more experienced radiologist (p >0.05).ConclusionsThe pretrained CNN models can automatically and accurately classify these two diseases and further help to improving clinical diagnostic performance.

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Section: Discussionmentioning

confidence: 94%

Differentiation of Glioma Mimicking Encephalitis and Encephalitis Using Multiparametric MR-Based Deep Learning

et al. 2021

Front. Oncol.

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“…In recent years, conventional and modern ML methods, especially neural networks and SVM, have illustrated the capability of processing oral cavity tumor-related image data. This includes oral cancer detection and tissue cell classification in the stage of cancer diagnosis ( Al-Ma’aitah & AlZubi, 2018 ; Aubreville et al, 2017 ; Das, Hussain & Mahanta, 2020 ; Jeyaraj & Samuel Nadar, 2019 ; Shamim et al, 2019 ), tumor margin assessment and tumor subtype classification in the process of clinical cancer treatment ( Fei et al, 2017 ; Marsden et al, 2020 ; van Rooij et al, 2019 ) and assessment of complications after treatment ( Ariji et al, 2019 ; Dong et al, 2018 ; Men et al, 2019 ). Major tumors like OSCC are able to be detected and evaluated with high accuracy using a timesaving algorithm ( Aubreville et al, 2017 ; Das, Hussain & Mahanta, 2020 ).…”

Section: Applications Of ML In the Dental Oral And Craniofacial Imaging Fieldmentioning

confidence: 99%

“…Some other studies have focused on the diagnosis of single oral cancers ( Aubreville et al, 2017 ; Das, Hussain & Mahanta, 2020 ; Rahman et al, 2020 ; Shamim et al, 2019 ). Squamous cell carcinoma is responsible for approximately 90% of total oral cancers and has become the sixth most common cancer worldwide ( D’Souza & Addepalli, 2018 ; Kar et al, 2020 ).…”

Section: Applications Of ML In the Dental Oral And Craniofacial Imaging Fieldmentioning

confidence: 99%

“…Biopsy is the current gold standard for OSCC ( Swinson et al, 2006 ), but the histopathological method is time-consuming and costly. Therefore, Navarun et al ( Das, Hussain & Mahanta, 2020 ) utilize four types of deep CNN models through a transfer learning approach, and one proposed CNN to achieve the automated histological grading of whole slide images on lesion locations.…”

Section: Applications Of ML In the Dental Oral And Craniofacial Imaging Fieldmentioning

confidence: 99%

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Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Ren

Luo

et al. 2021

PeerJ

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Artificial intelligence has been emerging as an increasingly important aspect of our daily lives and is widely applied in medical science. One major application of artificial intelligence in medical science is medical imaging. As a major component of artificial intelligence, many machine learning models are applied in medical diagnosis and treatment with the advancement of technology and medical imaging facilities. The popularity of convolutional neural network in dental, oral and craniofacial imaging is heightening, as it has been continually applied to a broader spectrum of scientific studies. Our manuscript reviews the fundamental principles and rationales behind machine learning, and summarizes its research progress and its recent applications specifically in dental, oral and craniofacial imaging. It also reviews the problems that remain to be resolved and evaluates the prospect of the future development of this field of scientific study.

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“…Additionally, they are served as the standard patterns of feature extraction based on CNN. As milestones in the design of CNN models, the ideas behind ResNet and DenseNet are also radiating beyond the natural image processing area [ 16 , 17 ]. At present, the research of feature extraction and feature fusion for specific task or specific data is still a hot topic in the field of computer vision.…”

Section: Introductionmentioning

confidence: 99%

Spatial–Spectral Feature Refinement for Hyperspectral Image Classification Based on Attention-Dense 3D-2D-CNN

Zhang

Feng-yuan

Feng

et al. 2020

Sensors

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Convolutional neural networks provide an ideal solution for hyperspectral image (HSI) classification. However, the classification effect is not satisfactory when limited training samples are available. Focused on “small sample” hyperspectral classification, we proposed a novel 3D-2D-convolutional neural network (CNN) model named AD-HybridSN (Attention-Dense-HybridSN). In our proposed model, a dense block was used to reuse shallow features and aimed at better exploiting hierarchical spatial–spectral features. Subsequent depth separable convolutional layers were used to discriminate the spatial information. Further refinement of spatial–spectral features was realized by the channel attention method and spatial attention method, which were performed behind every 3D convolutional layer and every 2D convolutional layer, respectively. Experiment results indicate that our proposed model can learn more discriminative spatial–spectral features using very few training data. In Indian Pines, Salinas and the University of Pavia, AD-HybridSN obtain 97.02%, 99.59% and 98.32% overall accuracy using only 5%, 1% and 1% labeled data for training, respectively, which are far better than all the contrast models.

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Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network

Cited by 127 publications

References 21 publications

Differentiation of Glioma Mimicking Encephalitis and Encephalitis Using Multiparametric MR-Based Deep Learning

Differentiation of Glioma Mimicking Encephalitis and Encephalitis Using Multiparametric MR-Based Deep Learning

Machine learning in dental, oral and craniofacial imaging: a review of recent progress

Spatial–Spectral Feature Refinement for Hyperspectral Image Classification Based on Attention-Dense 3D-2D-CNN

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