A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

Diniz, Débora Nasser; Rezende, Mariana; Bianchi, Andrea Gomes Campos; Carneiro, Cláudia Martins; Ushizima, Daniela; Medeiros, Fátima N. S. de; Souza, Marcone Jamilson Freitas

doi:10.3390/app11094091

Cited by 28 publications

(19 citation statements)

References 58 publications

(68 reference statements)

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“…A workload-reducing algorithm for analysis of cell nuclei features from Pap smear images. An investigation has been done with the involvement of eight traditional machine learning methods to perform a hierarchical classification [41] Morphological and other features are also included in the study. The study found that hierarchical classification provided better findings than those without it.…”

Section: Detection Methods Based On Cells/pap Smear Imagesmentioning

confidence: 99%

Improvement method for cervical cancer detection: A comparative analysis

Alias¹,

Mustafa²,

Jamlos³

et al. 2021

Oncology Research

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Cervical cancer is a prevalent and deadly cancer that affects women all over the world. It affects about 0.5 million women anually and results in over 0.3 million fatalities. Diagnosis of this cancer was previously done manually, which could result in false positives or negatives. The researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear images. Hence, this paper has reviewed several detection methods from the previous researches that has been done before. This paper reviews pre-processing, detection method framework for nucleus detection, and analysis performance of the method selected. There are four methods based on a reviewed technique from previous studies that have been running through the experimental procedure using Matlab, and the dataset used is established Herlev Dataset. The results show that the highest performance assessment metric values obtain from Method 1: Thresholding and Trace region boundaries in a binary image with the values of precision 1.0, sensitivity 98.77%, specificity 98.76%, accuracy 98.77% and PSNR 25.74% for a single type of cell. Meanwhile, the average values of precision were 0.99, sensitivity 90.71%, specificity 96.55%, accuracy 92.91% and PSNR 16.22%. The experimental results are then compared to the existing methods from previous studies. They show that the improvement method is able to detect the nucleus of the cell with higher performance assessment values. On the other hand, the majority of current approaches can be used with either a single or a large number of cervical cancer smear images. This study might persuade other researchers to recognize the value of some of the existing detection techniques and offer a strong approach for developing and implementing new solutions.

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Section: Detection Methods Based On Cells/pap Smear Imagesmentioning

confidence: 99%

Improvement method for cervical cancer detection: A comparative analysis

Alias¹,

Mustafa²,

Jamlos³

et al. 2021

Oncology Research

View full text Add to dashboard Cite

show abstract

“…It is a powerful supervised machine learning algorithm that is capable of performing both classification and regression tasks with high accuracy [18]- [20]. Various studies in the medical area have used RF algorithms to, for example, diagnose diabetes mellitus [21], [22], identify cervical cancer [23]- [25], or predict the risk of severity for COVID-19 patients at hospital admission [26].…”

Section: Model Trainingmentioning

confidence: 99%

A Screening System for COVID-19 Severity using Machine Learning

Yusuf¹,

Rosli²,

Yusop³

2022

IJACSA

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COVID-19 disease can be classified into various stages depending on the severity of the patient. Patients in severe stages of COVID-19 need immediate treatment and should be placed in a medical-ready environment because they are at high risk of death. Thus, hospitals need a fast and efficient method to screen large numbers of patients. The enormous amount of medical data in public repositories allows researchers to gain information and predict possible outcomes. In this study, we use a publicly available dataset from Springer Nature repository to discuss the performance of three machine learning techniques for prediction of severity of COVID-19: Random Forest (RF), Naïve Bayes (NB) and Gradient Boosting (GB). These techniques were selected for their good performance in medical predictive analytics. We measured the performance of the machine learning techniques using six measurements (accuracy, precision, recall, F1-score, sensitivity and specificity) in predicting COVID-19 severity. We found that RF generates the highest performance score, which is 78.4, compared with NB and GB. We also conducted experiments with RF to establish the critical symptoms in predicting COVID-19 severity, and the findings suggested that seven symptoms are substantial. Overall, the performance of various machine learning techniques to predict severity of COVID-19 using electronic health records indicates that machine learning can be successfully applied to determine specific treatment and effective triage.

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“…They evaluated the performance with the Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Random Forest (RF) algorithms, and 31 sets of characteristics. Isidoro et al [6] used a SVM to classify images of cervical cells obtained in Pap smear through the extraction of nongeometric characteristics, while Diniz et al [14] used a hierarchical methodology and geometric characteristics.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

et al. 2021

Self Cite

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In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.

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A Hierarchical Feature-Based Methodology to Perform Cervical Cancer Classification

Cited by 28 publications

References 58 publications

Improvement method for cervical cancer detection: A comparative analysis

Improvement method for cervical cancer detection: A comparative analysis

A Screening System for COVID-19 Severity using Machine Learning

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

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