CytoBrain: Cervical Cancer Screening System Based on Deep Learning Technology

Chen, Hua; Liu, Juan; Wen, Qing-Man; Zuo, Zhiqun; Liu, Jiasheng; Feng, Jing; Pang, Baochuan; Xiao, Di

doi:10.1007/s11390-021-0849-3

Cited by 61 publications

(32 citation statements)

References 32 publications

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“…Graph convolutional network 5 7 [21] 2021 SIPaKMeD Deep learning -ResNet-152 5 [22] 2021 SIPaKMeD Deep learning-Compact VGG 5 [23] 2021 SIPaKMeD Ensemble of CNN Models 2 [24] 2020 SIPaKMeD AlexNet 5…”

Section: Table 1 Summary Of Recent Work Done On Pap Smear Cytology Im...mentioning

confidence: 99%

Cervical cancer diagnosis based on cytology pap smear image classification using fractional coefficient and machine learning classifiers

Kalbhor¹,

Shinde²,

Jude

2022

TELKOMNIKA

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Doctors and pathologists have long been concerned about determining the malignancy from cell images. This task is laborious, time-consuming and needs expertise. Due to this reason, automated systems assist pathologists in providing a second opinion to arrive at accurate decision based on cytology images. The classification of cytology images has always been a difficult challenge among the various image analysis approaches due to its extreme intricacy. The thrust for early diagnosis of cervical cancer has always fuelled the research in medical image analysis for cancer detection. In this paper, an investigative study for the classification of cytology images is proposed. The proposed study uses the discrete coefficient transform (DCT) coefficient and Haar transform coefficients as features. These features are given as a input to seven different machine learning algorithms for normal and abnormal pap smear images classification. In order to optimize the feature size, fractional coefficients are used to form the five different sizes of feature vectors. In the proposed work, DCT transform has given the highest classification accuracy of 81.11%. Comparing the different machine learning algorithms the overall best performance is given by the random forest classifier.

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Section: Table 1 Summary Of Recent Work Done On Pap Smear Cytology Im...mentioning

confidence: 99%

Cervical cancer diagnosis based on cytology pap smear image classification using fractional coefficient and machine learning classifiers

Kalbhor¹,

Shinde²,

Jude

2022

TELKOMNIKA

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“…Their architecture outperforms the current ML models. Chen et al ( 11 ) developed Cyto Brain that facilitates in subsequent clinical diagnosis, an artificial intelligence (AI) based system. CytoBrain consists of three main modules: (1) to extract only cell images in a whole slide image efficiently, cervical cell segmentation module has been designed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Machine learning (ML) and deep learning (DL) are being utilized for brain tumor detection, cervical cancer detection, breast cancer detection, COVID detection, physical activity recognition, thermal sensation detection, and cognitive health assessment of dementia individuals ( 1 – 3 , 3 – 10 ). Advancements in Health Care Industry makes it more effective than traditional diagnosing techniques ( 11 – 14 ). According to medical reports published by Global cancer statistics ( 15 ) every year, 493,000 cervical malignancy patients have been added, among which 15% are female malignancy patients.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Assisted Cervical Cancer Detection

Mehmood

Rizwan

ml.

et al. 2021

Front. Public Health

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Cervical malignant growth is the fourth most typical reason for disease demise in women around the globe. Cervical cancer growth is related to human papillomavirus (HPV) contamination. Early screening made cervical cancer a preventable disease that results in minimizing the global burden of cervical cancer. In developing countries, women do not approach sufficient screening programs because of the costly procedures to undergo examination regularly, scarce awareness, and lack of access to the medical center. In this manner, the expectation of the individual patient's risk becomes very high. There are many risk factors relevant to malignant cervical formation. This paper proposes an approach named CervDetect that uses machine learning algorithms to evaluate the risk elements of malignant cervical formation. CervDetect uses Pearson correlation between input variables as well as with the output variable to pre-process the data. CervDetect uses the random forest (RF) feature selection technique to select significant features. Finally, CervDetect uses a hybrid approach by combining RF and shallow neural networks to detect Cervical Cancer. Results show that CervDetect accurately predicts cervical cancer, outperforms the state-of-the-art studies, and achieved an accuracy of 93.6%, mean squared error (MSE) error of 0.07111, false-positive rate (FPR) of 6.4%, and false-negative rate (FNR) of 100%.

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“…The advent of WSIs led to the application of medical image analysis techniques, machine learning, and deep learning techniques for aiding pathologists in inspecting WSIs. Deep-learning-based applications ranged from tasks, such as cancer diagnosis from WSIs, cell classification, and segmentation of nuclei, to patient stratification and outcome prediction [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. For cytology, in particular, only recently have there been investigations for applying deep learning on large datasets of cervical WSIs Holmström et al [ 45 ], Lin et al [ 46 ], Cheng et al [ 47 ].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images

Kanavati

Hirose

Ishii

et al. 2022

Cancers

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Liquid-based cytology (LBC) for cervical cancer screening is now more common than the conventional smears, which when digitised from glass slides into whole-slide images (WSIs), opens up the possibility of artificial intelligence (AI)-based automated image analysis. Since conventional screening processes by cytoscreeners and cytopathologists using microscopes is limited in terms of human resources, it is important to develop new computational techniques that can automatically and rapidly diagnose a large amount of specimens without delay, which would be of great benefit for clinical laboratories and hospitals. The goal of this study was to investigate the use of a deep learning model for the classification of WSIs of LBC specimens into neoplastic and non-neoplastic. To do so, we used a dataset of 1605 cervical WSIs. We evaluated the model on three test sets with a combined total of 1468 WSIs, achieving ROC AUCs for WSI diagnosis in the range of 0.89–0.96, demonstrating the promising potential use of such models for aiding screening processes.

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CytoBrain: Cervical Cancer Screening System Based on Deep Learning Technology

Cited by 61 publications

References 32 publications

Cervical cancer diagnosis based on cytology pap smear image classification using fractional coefficient and machine learning classifiers

Cervical cancer diagnosis based on cytology pap smear image classification using fractional coefficient and machine learning classifiers

Machine Learning Assisted Cervical Cancer Detection

A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images

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