Machine learning-based statistical analysis for early stage detection of cervical cancer

Novel methods and materials are used in healthcare applications for finding cancer in various parts of the human system. To select the most suitable therapy plan for individuals with domestically progressed cervical cancer, robustness metrics are required to estimate their early phase. The goal of the research is to increase the effectiveness of cervical cancer patients' detection by using deep learning-based radiomics assessment of magnetic resonance imaging (MRI). From March 2016 to November 2019, 125 patients with early-stage cervical cancer provided 980 dynamic X1 contrast-enhanced (X1DCE) and 850 X2 weighted imaging (X2WI) MRI images for training and testing. A convolutional neural network model was used to estimate cervical cancer state based on the specified characteristics. The X1DCE exhibited high discriminative outcomes than X2WI MRI in terms of prediction ability, as calculated by the confusion matrix assessment and receiver operating characteristic (ROC) curve approach. The mean maximum region under the curve of 0.95 was found using an attentive ensemble learning method that included both MRI sequencing (Sensitivity = 0.94, Specificity = 0.94, and accuracy = 0.96). Whenever compared with conventional radiomic approaches, the results show that a variety of radiomics based on deep learning might be created to help radiologists anticipate vascular invasion in patients with cervical cancer before surgery. Based on radiomics technique, it has proven to be an effective tool for estimating cervical cancer in its early stages. It would help people choose the best therapy method for them and make medical judgments.

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“…Diagnose cervical cancer in its early stages. is method is inefficient and difficult to predict the exact value [21].…”

Section: Related Workmentioning

confidence: 99%

Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach

Pareek¹,

Prasad

et al. 2022

Advances in Materials Science and Engineering

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“…The random tree classifier showed better results for cytology (98.65 %) and biopsy (98.33 %), whereas the Instance-Based K-nearest neighbour (IBK) with random forest classifier provided higher accuracy for Hinselmann (99.16 %) and Schiller (98.58 %). 22 Nithya et al predict cervical cancer using random forest, rpart, C 5.0, KNN and SVM algorithms after optimised feature selection. Contrary to the present study, the random forest and C 5.0 classifier models showed higher accuracy in predicting cervical cancer.…”

Section: Discussionmentioning

confidence: 99%

“…Both logarithmic and sine functions were superior for the Hinselmann dataset, while Z-score performed best for the Schiller dataset. 22 Similarly, Fernandes et al proposed a computationally automated strategy to predict biopsy results from cervical risk factors. The strategy consists of joint and fully supervised optimisation of dimensionality reduction.…”

Section: Discussionmentioning

confidence: 99%

Optimised feature selection and cervical cancer prediction using Machine learning classification

Tak¹,

Parihar²,

Singh³

et al. 2022

Scripta Medica

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Background: Screening and early detection play a key role in cervical cancer prevention. The present study predicts the outcome of various diagnostic tests used to diagnose cervical cancer using machine learning algorithms. Methods: The present study ran various cervical cancer risk factors on a machine learning (ML) classifier to predict outcomes of Hinselmann, Schiller, cytology and biopsy. The dataset is publicly available on the Machine Learning Repository website of the University of California Irvine. The imbalanced dataset was pre-processed using oversampling methods. The significantly varied features between the two levels of a response variable were used to train the machine learning classifiers on MATLAB. The classifiers used were Decision Trees, Support Vector Machine, K-Nearest Neighbours and Ensemble learning classifiers. The performance metrics of the classifiers were expressed as accuracy, the area under the receiver operator characteristic (AU-ROC) curve, sensitivity and specificity. Results: The Fine Gaussian SVM classifier was the best to classify Hinselmann, cytology and biopsy with the accuracy of 97.5 %, 62.5 % and 98 %, respectively. However, Boosted trees performed best in the classification of Schiller with 81.3 % accuracy. Conclusion: The present study selected optimised features among multiple risk factors to train various ML classifiers to predict cervical cancer.

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“…These technical models, combined with other data sets or with clinical data, have greatly expanded the scope of application. 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 ].…”

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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Machine learning-based statistical analysis for early stage detection of cervical cancer

Cited by 56 publications

References 34 publications

Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach

Predicting the Spread of Vessels in Initial Stage Cervical Cancer through Radiomics Strategy Based on Deep Learning Approach

Optimised feature selection and cervical cancer prediction using Machine learning classification

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

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