Predictions of cervical cancer identification by photonic method combined with machine learning

Kruczkowski, Michał; Drabik-Kruczkowska, Anna; Marciniak, Anna; Tarczewska, Martyna; Kosowska, Monika; Jędrzejewska‐Szczerska, Małgorzata

doi:10.1038/s41598-022-07723-1

Cited by 28 publications

(12 citation statements)

References 56 publications

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“…In Kruczkowski et al’s study, the time needed to train for the Naïve Bayes and CNN algorithms varied from 7.54 ms to 5320 ms. The prediction time for cervical cancer by the Naïve Bayes and RF algorithms varied from 1.81 ms to 15.5 ms, and the accuracies differed [ 76 ]. Elakkiya et al reported an average of 0.2 s to classify the cervical lesion using a hybrid deep learning technique that combined small-object detection generative adversarial networks (SOD-GAN) and the fine-tuned stacked autoencoder (F-SAE).…”

Section: Resultsmentioning

confidence: 99%

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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Objective: The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. Materials and Methods: Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Results: The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80–100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9–98.22% and 51.8–96.2%, respectively. Conclusion: The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.

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Section: Resultsmentioning

confidence: 99%

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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“…Over the past decade, researchers have advocated the use of MLP in cervical cancer prediction due to its respectable classification accuracy [ 10 , 18 , 19 , 20 , 25 , 26 , 29 , 30 ]; therefore, MLP is selected for optimization by GA in this study. In addition, various researchers used SVM, RF, LR DT, KNN, NB, LDA, and AdaBoost to classify cervical cancer [ 4 , 9 , 10 , 11 , 13 , 22 , 23 , 30 , 31 , 32 , 33 ]. Those algorithms are among the most common classification algorithms in modeling medical datasets.…”

Section: Methodsmentioning

confidence: 99%

“…Feature selection is the process of minimizing the number of input variables when creating a predictive model. The number of input variables might be reduced to decrease the computational cost of modeling and, in some circumstances, to improve the model’s performance [ 33 , 34 ].…”

Section: Methodsmentioning

confidence: 99%

Cervical Cancer Diagnosis Using an Integrated System of Principal Component Analysis, Genetic Algorithm, and Multilayer Perceptron

Dweekat

Lam

2022

Healthcare

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Cervical cancer is one of the most dangerous diseases that affect women worldwide. The diagnosis of cervical cancer is challenging, costly, and time-consuming. Existing literature has focused on traditional machine learning techniques and deep learning to identify and predict cervical cancer. This research proposes an integrated system of Genetic Algorithm (GA), Multilayer Perceptron (MLP), and Principal Component Analysis (PCA) that accurately predicts cervical cancer. GA is used to optimize the MLP hyperparameters, and the MLPs act as simulators within the GA to provide the prediction accuracy of the solutions. The proposed method uses PCA to transform the available factors; the transformed features are subsequently used as inputs to the MLP for model training. To contrast with the PCA method, different subsets of the original factors are selected. The performance of the integrated system of PCA–GA–MLP is compared with nine different classification algorithms. The results indicate that the proposed method outperforms the studied classification algorithms. The PCA–GA–MLP model achieves the best accuracy in diagnosing Hinselmann, Biopsy, and Cytology when compared to existing approaches in the literature that were implemented on the same dataset. This study introduces a robust tool that allows medical teams to predict cervical cancer in its early stage.

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“…For example, various Feature Selection Technique (FST) methods were applied to the transformed datasets to predict cervical cancer or identify important risk factors [ 75 , 76 ]. The machine learning algorithm is fused with an optoelectronic sensor to realise rapid sample measurement and the automatic classification of results [ 77 ]. Several meta-analyses confirmed the diagnostic performance of machine learning or deep learning algorithms in cervical cancer recognition [ 78 , 79 ].…”

Section: Detection Of Premalignancy and Malignancy Of The Uterine Cervixmentioning

confidence: 99%

Review of the Standard and Advanced Screening, Staging Systems and Treatment Modalities for Cervical Cancer

Boon

Luk

Xiao

et al. 2022

Cancers

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Cancer arising from the uterine cervix is the fourth most common cause of cancer death among women worldwide. Almost 90% of cervical cancer mortality has occurred in low- and middle-income countries. One of the major aetiologies contributing to cervical cancer is the persistent infection by the cancer-causing types of the human papillomavirus. The disease is preventable if the premalignant lesion is detected early and managed effectively. In this review, we outlined the standard guidelines that have been introduced and implemented worldwide for decades, including the cytology, the HPV detection and genotyping, and the immunostaining of surrogate markers. In addition, the staging system used to classify the premalignancy and malignancy of the uterine cervix, as well as the safety and efficacy of the various treatment modalities in clinical trials for cervical cancers, are also discussed. In this millennial world, the advancements in computer-aided technology, including robotic modules and artificial intelligence (AI), are also incorporated into the screening, diagnostic, and treatment platforms. These innovations reduce the dependence on specialists and technologists, as well as the work burden and time incurred for sample processing. However, concerns over the practicality of these advancements remain, due to the high cost, lack of flexibility, and the judgment of a trained professional that is currently not replaceable by a machine.

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Predictions of cervical cancer identification by photonic method combined with machine learning

Cited by 28 publications

References 56 publications

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

Cervical Cancer Diagnosis Using an Integrated System of Principal Component Analysis, Genetic Algorithm, and Multilayer Perceptron

Review of the Standard and Advanced Screening, Staging Systems and Treatment Modalities for Cervical Cancer

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