Dataset Information

High-Accuracy Oral Squamous Cell Carcinoma Auxiliary Diagnosis System Based on EfficientNet.

ABSTRACT: It is important to diagnose the grade of oral squamous cell carcinoma (OSCC), but the current evaluation of the biopsy slide still mainly depends on the manual operation of pathologists. The workload of manual evaluation is large, and the results are greatly affected by the subjectivity of the pathologists. In recent years, with the development and application of deep learning, automatic evaluation of biopsy slides is gradually being applied to medical diagnoses, and it has shown good results. Therefore, a new OSCC auxiliary diagnostic system was proposed to automatically and accurately evaluate the patients' tissue slides. This is the first study that compared the effects of different resolutions on the results. The OSCC tissue slides from The Cancer Genome Atlas (TCGA, n=697) and our independent datasets (n=337) were used for model training and verification. In the test dataset of tiles, the accuracy was 93.1% at 20x resolution (n=306,134), which was higher than that at 10x (n=154,148, accuracy=90.9%) and at 40x (n=890,681, accuracy=89.3%). The accuracy of the new system based on EfficientNet, which was used to evaluate the tumor grade of the biopsy slide, reached 98.1% [95% confidence interval (CI): 97.1% to 99.1%], and the area under the receiver operating characteristic curve (AUROC) reached 0.998 (95%CI: 0.995 to 1.000) in the TCGA dataset. When verifying the model on the independent image dataset, the accuracy still reached 91.4% (95% CI: 88.4% to 94.4%, at 20x) and the AUROC reached 0.992 (95%CI: 0.982 to 1.000). It may benefit oral pathologists by reducing certain repetitive and time-consuming tasks, improving the efficiency of diagnosis, and facilitating the further development of computational histopathology.

SUBMITTER: Xu Z

PROVIDER: S-EPMC9302026 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

High-Accuracy Oral Squamous Cell Carcinoma Auxiliary Diagnosis System Based on EfficientNet.

Xu Ziang Z Peng Jiakuan J Zeng Xin X Xu Hao H Chen Qianming Q

Frontiers in oncology 20220707

It is important to diagnose the grade of oral squamous cell carcinoma (OSCC), but the current evaluation of the biopsy slide still mainly depends on the manual operation of pathologists. The workload of manual evaluation is large, and the results are greatly affected by the subjectivity of the pathologists. In recent years, with the development and application of deep learning, automatic evaluation of biopsy slides is gradually being applied to medical diagnoses, and it has shown good results. T ...[more]

PMID: 35875067

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Project description:IntroductionOral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accuracy and efficiency, highlighting the need for more reliable approaches. This study aims to explore the discriminative potential of histopathological images of oral epithelium and OSCC. By utilizing a database containing 1224 images from 230 patients, captured at varying magnifications and publicly available, a customized deep learning model based on EfficientNetB3 was developed. The model's objective was to differentiate between normal epithelium and OSCC tissues by employing advanced techniques such as data augmentation, regularization, and optimization.MethodsThe research utilized a histopathological imaging database for Oral Cancer analysis, incorporating 1224 images from 230 patients. These images, taken at various magnifications, formed the basis for training a specialized deep learning model built upon the EfficientNetB3 architecture. The model underwent training to distinguish between normal epithelium and OSCC tissues, employing sophisticated methodologies including data augmentation, regularization techniques, and optimization strategies.ResultsThe customized deep learning model achieved significant success, showcasing a remarkable 99% accuracy when tested on the dataset. This high accuracy underscores the model's efficacy in effectively discerning between normal epithelium and OSCC tissues. Furthermore, the model exhibited impressive precision, recall, and F1-score metrics, reinforcing its potential as a robust diagnostic tool for OSCC.DiscussionThis research demonstrates the promising potential of employing deep learning models to address the diagnostic challenges associated with OSCC. The model's ability to achieve a 99% accuracy rate on the test dataset signifies a considerable leap forward in earlier and more accurate detection of OSCC. Leveraging advanced techniques in machine learning, such as data augmentation and optimization, has shown promising results in improving patient outcomes through timely and precise identification of OSCC.

Project description:Background and aimMelasma (ML), naevus fusco-caeruleus zygomaticus (NZ), freckles (FC), cafe-au-lait spots (CS), nevus of ota (NO), and lentigo simplex (LS), are common skin diseases causing hyperpigmentation. Deep learning algorithms learn the inherent laws and representation levels of sample data and can analyze the internal details of the image and classify it objectively to be used for image diagnosis. However, deep learning algorithms that can assist clinicians in diagnosing skin hyperpigmentation conditions are lacking.MethodsThe optimal deep-learning image recognition algorithm was explored for the auxiliary diagnosis of hyperpigmented skin disease. Pretrained models, such as VGG-19, GoogLeNet, InceptionV3, ResNet50V2, ResNet101V2, ResNet152V2, InceptionResNetV2, DesseNet201, MobileNet, and NASNetMobile were used to classify images of six common hyperpigmented skin diseases. The best deep learning algorithm for developing an online clinical diagnosis system was selected by using accuracy and area under curve (AUC) as evaluation indicators.ResultsIn this research, the parameters of the above-mentioned ten deep learning algorithms were 18333510, 5979702, 21815078, 23577094, 42638854, 58343942, 54345958, 18333510, 3235014, and 4276058, respectively, and their training time was 380, 162, 199, 188, 315, 511, 471, 697, 101, and 144 min respectively. The respective accuracies of the training set were 85.94%, 99.72%, 99.61%, 99.52%, 99.52%, 98.84%, 99.61%, 99.13%, 99.52%, and 99.61%. The accuracy rates of the test set data were 73.28%, 57.40%, 70.04%, 71.48%, 68.23%, 71.11%, 71.84%, 73.28%, 70.39%, and 43.68%, respectively. Finally, the areas of AUC curves were 0.93, 0.86, 0.93, 0.91, 0.91, 0.92, 0.93, 0.92, 0.93, and 0.82, respectively.ConclusionsThe experimental parameters, training time, accuracy, and AUC of the above models suggest that MobileNet provides a good clinical application prospect in the auxiliary diagnosis of hyperpigmented skin.

Dataset Information

High-Accuracy Oral Squamous Cell Carcinoma Auxiliary Diagnosis System Based on EfficientNet.

Publications

High-Accuracy Oral Squamous Cell Carcinoma Auxiliary Diagnosis System Based on EfficientNet.

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