Deep learning-based approaches for robust classification of cervical cancer

Pacal, Ishak; Kılıçarslan, Serhat

doi:10.1007/s00521-023-08757-w

Cited by 49 publications

(13 citation statements)

References 61 publications

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“…Researchers applied deep learning models to predict the survival rate of cervical cancer patients [ 52 ]. Authors applied deep learning models on the pap-smear dataset to classify cervical cancer [ 53 ]. Pretrained models like AlexNet, GoogleNet and ResNET are employed for feature extraction and machine learning models are used for classification using Pap smear dataset for cervical cancer prediction [ 54 ].…”

Section: Related Workmentioning

confidence: 99%

Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning

Aljrees

2024

PLoS ONE

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Cervical cancer is a leading cause of women’s mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification—handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of 0.97. This study examines three distinct scenarios: one involving the deletion of missing values, another utilizing KNN imputation, and a third employing PCA for imputing missing values. This research has significant implications for the medical field, offering medical experts a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures. By addressing missing data challenges and achieving high accuracy, this work represents a valuable contribution to cervical cancer detection, ultimately aiming to reduce the impact of this disease on women’s health and healthcare systems.

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Section: Related Workmentioning

confidence: 99%

Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning

Aljrees

2024

PLoS ONE

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“…The study Pacal and Kılıcarslan 45 provides a comprehensive analysis of 40 CNN‐based models and over 20 ViT‐based models on the Sipakmed pap smear dataset. The experimental findings indicate that the most recent ViT‐based models exhibit superior performance, while the CNN models currently in use demonstrate comparable effectiveness to the ViT models.…”

Section: Prior Artmentioning

confidence: 99%

CerviFormer: A pap smear‐based cervical cancer classification method using cross‐attention and latent transformer

Deo,

Pal,

Panigrahi

et al. 2024

Int J Imaging Syst Tech

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Cervical cancer is one of the primary causes of death in women. It should be diagnosed early and treated according to the best medical advice, similar to other diseases, to ensure that its effects are as minimal as possible. Pap smear images are one of the most constructive ways for identifying this type of cancer. This study proposes a cross‐attention‐based Transfomer approach for the reliable classification of cervical cancer in pap smear images. In this study, we propose the CerviFormer‐a model that depends on the Transformers and thereby requires minimal architectural assumptions about the size of the input data. The model uses a cross‐attention technique to repeatedly consolidate the input data into a compact latent Transformer module, which enables it to manage very large‐scale inputs. We evaluated our model on two publicly available pap smear datasets. For 3‐state classification on the Sipakmed data, the model achieved an accuracy of 96.67%. For 2‐state classification on the Herlev data, the model achieved an accuracy of 94.57%. Experimental results on two publicly accessible datasets demonstrate that the proposed method achieves competitive results when compared to contemporary approaches. The proposed method brings forth a comprehensive classification model to detect cervical cancer in pap smear images. This may aid medical professionals in providing better cervical cancer treatment, consequently, enhancing the overall effectiveness of the entire testing process.

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“…The model also integrated data augmentation for class balance and sample augmentation. Pacal and Kılıcarslan 25 implement advanced DL models, including CNN and vision transformer (ViT) methods, together with data augmentation and ensemble learning methods. In 26 , the Multi-Axis Vision Transformer (MaxViT) model is presented and optimized for Pap smear data, integrating ConvNeXtv2 and GRN-based MLPs models.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced cervical precancerous lesions detection and classification using Archimedes Optimization Algorithm with transfer learning

Allogmani,

Mohamed,

Al-shibly

et al. 2024

Sci Rep

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Cervical cancer (CC) ranks as the fourth most common form of cancer affecting women, manifesting in the cervix. CC is caused by the Human papillomavirus (HPV) infection and is eradicated by vaccinating women from an early age. However, limited medical facilities present a significant challenge in mid- or low-income countries. It can improve the survivability rate and be successfully treated if the CC is detected at earlier stages. Current technological improvements allow for cost-effective, more sensitive, and rapid screening and treatment measures for CC. DL techniques are widely adopted for the automated detection of CC. DL techniques and architectures are used to detect CC and provide higher detection performance. This study offers the design of Enhanced Cervical Precancerous Lesions Detection and Classification using the Archimedes Optimization Algorithm with Transfer Learning (CPLDC-AOATL) algorithm. The CPLDC-AOATL algorithm aims to diagnose cervical cancer using medical images. At the preliminary stage, the CPLDC-AOATL technique involves a bilateral filtering (BF) technique to eliminate the noise in the input images. Besides, the CPLDC-AOATL technique applies the Inception-ResNetv2 model for the feature extraction process, and the use of AOA chose the hyperparameters. The CPLDC-AOATL technique involves a bidirectional long short-term memory (BiLSTM) model for the cancer detection process. The experimental outcome of the CPLDC-AOATL technique emphasized the superior accuracy outcome of 99.53% over other existing approaches under a benchmark dataset.

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Deep learning-based approaches for robust classification of cervical cancer

Cited by 49 publications

References 61 publications

Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning

Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning

CerviFormer: A pap smear‐based cervical cancer classification method using cross‐attention and latent transformer

Enhanced cervical precancerous lesions detection and classification using Archimedes Optimization Algorithm with transfer learning

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