Deep learning model for classifying endometrial lesions

Zhang, YunZheng; Wang, ZiHao; Zhang, Jin; Wang, Cuicui; Wang, YuShan; Chen, Hao; Shan, LuHe; Huo, Jianing; Gu, Jiahui; Ma, Xiaolong

doi:10.1186/s12967-020-02660-x

Cited by 53 publications

(33 citation statements)

References 22 publications

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“…ML-aided approaches to examine EC not only increase accuracy but also provide a minimally invasive and less expensive tool to correctly diagnose EC. A study conducted by Zhang et al developed a CNN-based computer-aided diagnosis system using the VGGNet-16 model for diagnostic hysteroscopy image classification (96). Using 1,851 hysteroscopic images of uterine patients as input, Zhang et al also investigated the VGGNet-16 CNN model efficiency for the classification of endometrial lesions.…”

Section: Model For Classifying Endometrial Lesionsmentioning

confidence: 99%

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Machine Learning for Endometrial Cancer Prediction and Prognostication

et al. 2022

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Endometrial cancer (EC) is a prevalent uterine cancer that remains a major contributor to cancer-associated morbidity and mortality. EC diagnosed at advanced stages shows a poor therapeutic response. The clinically utilized EC diagnostic approaches are costly, time-consuming, and are not readily available to all patients. The rapid growth in computational biology has enticed substantial research attention from both data scientists and oncologists, leading to the development of rapid and cost-effective computer-aided cancer surveillance systems. Machine learning (ML), a subcategory of artificial intelligence, provides opportunities for drug discovery, early cancer diagnosis, effective treatment, and choice of treatment modalities. The application of ML approaches in EC diagnosis, therapies, and prognosis may be particularly relevant. Considering the significance of customized treatment and the growing trend of using ML approaches in cancer prediction and monitoring, a critical survey of ML utility in EC may provide impetus research in EC and assist oncologists, molecular biologists, biomedical engineers, and bioinformaticians to further collaborative research in EC. In this review, an overview of EC along with risk factors and diagnostic methods is discussed, followed by a comprehensive analysis of the potential ML modalities for prevention, screening, detection, and prognosis of EC patients.

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Section: Model For Classifying Endometrial Lesionsmentioning

confidence: 99%

“…A study conducted by Zhang et al. developed a CNN-based computer-aided diagnosis system using the VGGNet-16 model for diagnostic hysteroscopy image classification ( 96 ). Using 1,851 hysteroscopic images of uterine patients as input, Zhang et al.…”

Section: Application In Ec Prediction Diagnosis and Prognosismentioning

confidence: 99%

Machine Learning for Endometrial Cancer Prediction and Prognostication

et al. 2022

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“…RNN has the challenge in the meantime of training long-term addiction data that is solved in one of the RNN variants. The advance version of the RNN network was employed by LSTM foreseen by Hochreiter&Schmidhuber [33], overcoming the RNN limitations using the hidden layer unit known as memory cells, which store the temporary condition of the network, and which are controlled by three portals: input gate, output gate and forget gate [34]. The work of the input gate and output gate controls the memory cell input and output flow through the remaining network.…”

Section: Classification Using Lstm and Bi-lstmmentioning

confidence: 99%

A Novel Whale Optimized TGV-FCMS Segmentation With Modified LSTM Classification for Endometrium Cancer Prediction

Kiranmai¹,

P.V.²

2022

INDJCSE

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Early detection of endometrial carcinoma in uterus is essential for effective treatment. Endometrial carcinoma is the worst kind of endometrium cancer among the others since it is considerably more likely to affect the additional parts of the body if not detected and treated early. Non-invasive medical computer vision, also known as medical image processing, is becoming increasingly essential in the clinical diagnosis of various diseases. Such techniques provide a tool for automatic image processing, allowing for an accurate and timely assessment of the lesion. One of the most difficult aspects of developing an effective automatic categorization system is the absence of huge datasets. Using image processing and deep learning, this article presented an artificial endometrium cancer diagnosis system. The processes in this study include gathering a dermoscopy images from the database, preprocessing, segmentation using hybrid Fuzzy C-Means (FCM) and optimizing the weights using the Whale Optimization Algorithm (WOA). The characteristics of the damaged endometrium cells are retrieved using the feature extraction approach after the Magnetic Resonance pictures have been segmented. The collected characteristics are classified using a deep learning-based methodology called Long Short-Term Memory (LSTM) and Bi-directional LSTM classifiers. After using the publicly accessible data set, suggested classifiers obtain an accuracy of 97% and segmentation accuracy of 93%.

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“…Gynecologic cancer has also embraced new technology in terms of predicting recurrence and developing prognosis models [16,17]. Furthermore, imaging study substitutions for diagnostic aids and lesion classification aids have recently been evaluated [18][19][20][21]. Among the most common Lynch syndrome-related cancer CRC studies are those using Lynch syndrome-screening markers and artificial intelligence, which has demonstrated that machine learning can predict MSI/dMMR with high accuracy in high-quality, curated datasets [22].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers

Kim

Choi

Kim

et al. 2021

Cancers

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To support the implementation of genome-based precision medicine, we developed machine learning models that predict the recurrence of patients with gynecologic cancer in using immune checkpoint inhibitors (ICI) based on clinical and pathologic characteristics, including Lynch syndrome-related screening markers such as immunohistochemistry (IHC) and microsatellite instability (MSI) tests. To accomplish our goal, we reviewed the patient demographics, clinical data, and pathological results from their medical records. Then we identified seven potential characteristics (four MMR IHC [MLH1, MSH2, MSH6, and PMS2], MSI, Age 60, and tumor size). Following that, predictive models were built based on these variables using six machine learning algorithms: logistic regression (LR), support vector machine (SVM), naive Bayes (NB), random forest (RF), gradient boosting (GB), and extreme gradient boosting (EGB) (XGBoost). The experimental results showed that the RF-based model performed best at predicting gynecologic cancer recurrence, with AUCs of 0.818 and 0.826 for the 5-fold cross-validation (CV) and 5-fold CV with 10 repetitions, respectively. This study provides novel and baseline results about predicting the recurrence of gynecologic cancer in patients using ICI by using machine learning methods based on Lynch syndrome-related screening markers.

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Deep learning model for classifying endometrial lesions

Cited by 53 publications

References 22 publications

Machine Learning for Endometrial Cancer Prediction and Prognostication

Machine Learning for Endometrial Cancer Prediction and Prognostication

A Novel Whale Optimized TGV-FCMS Segmentation With Modified LSTM Classification for Endometrium Cancer Prediction

Machine Learning for Recurrence Prediction of Gynecologic Cancers Using Lynch Syndrome-Related Screening Markers

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