Pap Smear Images Classification Using Machine Learning: A Literature Matrix

Alias, Nur Ain; Mustafa, Wan Azani; Jamlos, Mohd Aminudin; Alquran, Hiam; Hanafi, Hafizul Fahri; Ismail, Shahrina; Rahman, Khairul Shakir Ab

doi:10.3390/diagnostics12122900

Cited by 11 publications

(2 citation statements)

References 43 publications

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“…Ling et al (2018) [33] proposed a deep convolutional neural network called DeepPap for the classification of cervical cell images. In addition, Nur Ain et al (2022) [34] conducted a review of the literature related to the classification of pap smear cell images for cervical cancer diagnosis.…”

Section: Related Workmentioning

confidence: 99%

New Ensemble Deep Learning Model for Gynaecological Cancer Risk Prediction

Berahman,

Zhou,

et al. 2024

Preprint

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Gynaecological cancer is a prevalent type of cancer affecting women worldwide, and early detection is critical for successful treatment and prevention of cancer recurrence. In our study, we introduce an innovative method to predict cervical cancer risk using a combination of deep learning models. Our approach employs a nested ensemble technique, where multiple deep learning base models are individually trained and then integrated to create a more precise ensemble model. In the initial phase of our nested ensemble method, we leverage sophisticated stacking deep learning techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Support Vector Machines (SVMs). The subsequent MetaClassifier section introduces a voting mechanism, seamlessly integrating methods such as j48 and SGD. Our experimental findings reveal that our method surpasses expectations, demonstrating superior performance compared to existing state-of-the-art approaches in analyzing cervical cancer data.

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Section: Related Workmentioning

confidence: 99%

New Ensemble Deep Learning Model for Gynaecological Cancer Risk Prediction

Berahman,

Zhou,

et al. 2024

Preprint

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“…By analyzing cell images and categorizing the cells into one of seven classes, Pap smears for advanced cervical screening are a highly effective technique for precancerous cell detection. Computer-aided medical imaging systems have considerably benefited from remarkable advances in artificial intelligence (AI) technology [9]. Thus, this paper aims to take advantage of convolutional neural network (CNN) architectures and machine learning to analyze the classification of cervical Pap smear images to improve the reliability of the test results.…”

Section: Introductionmentioning

confidence: 99%

A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images

Alsalatie,

Alquran,

Mustafa

et al. 2023

Diagnostics

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One of the most widespread health issues affecting women is cervical cancer. Early detection of cervical cancer through improved screening strategies will reduce cervical cancer-related morbidity and mortality rates worldwide. Using a Pap smear image is a novel method for detecting cervical cancer. Previous studies have focused on whole Pap smear images or extracted nuclei to detect cervical cancer. In this paper, we compared three scenarios of the entire cell, cytoplasm region, or nucleus region only into seven classes of cervical cancer. After applying image augmentation to solve imbalanced data problems, automated features are extracted using three pre-trained convolutional neural networks: AlexNet, DarkNet 19, and NasNet. There are twenty-one features as a result of these scenario combinations. The most important features are split into ten features by the principal component analysis, which reduces the dimensionality. This study employs feature weighting to create an efficient computer-aided cervical cancer diagnosis system. The optimization procedure uses the new evolutionary algorithms known as Ant lion optimization (ALO) and particle swarm optimization (PSO). Finally, two types of machine learning algorithms, support vector machine classifier, and random forest classifier, have been used in this paper to perform classification jobs. With a 99.5% accuracy rate for seven classes using the PSO algorithm, the SVM classifier outperformed the RF, which had a 98.9% accuracy rate in the same region. Our outcome is superior to other studies that used seven classes because of this focus on the tissues rather than just the nucleus. This method will aid physicians in diagnosing precancerous and early-stage cervical cancer by depending on the tissues, rather than on the nucleus. The result can be enhanced using a significant amount of data.

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