Classification of cervical cancer using Dense CapsNet with Seg-UNet and denoising autoencoders

Cervical cancer is one of the deadly diseases that affects women, which requires periodic examinations to identify and treat any cancerous tumors at a preliminary stage. The most prevalent examination tool for cervical cancer prompt identification is the cervical smear (Pap smear) testing; however,...

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Published in:	Scientific reports Vol. 14; no. 1; pp. 31764 - 15
Main Authors:	Yang, Hui, Aydi, Walid, Innab, Nisreen, Ghoneim, Mohamed E., Ferrara, Massimiliano
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 30.12.2024 Nature Publishing Group Nature Portfolio
Subjects:	692/699/75 692/700/228 Algorithms Autoencoders Cervical cancer Classification Deep Learning Dense CapsNet Female Humanities and Social Sciences Humans Image processing Image Processing, Computer-Assisted - methods Machine Learning Medical research multidisciplinary Negligence Neural Networks, Computer Probability learning Science Science (multidisciplinary) Segmentation Uterine Cervical Neoplasms - classification Uterine Cervical Neoplasms - diagnosis Uterine Cervical Neoplasms - pathology Deep learning Dense CapsNet Segmentation Autoencoders Cervical cancer
ISSN:	2045-2322, 2045-2322
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Abstract	Cervical cancer is one of the deadly diseases that affects women, which requires periodic examinations to identify and treat any cancerous tumors at a preliminary stage. The most prevalent examination tool for cervical cancer prompt identification is the cervical smear (Pap smear) testing; however, due to human negligence, this examination method has an elevated probability of negative findings. Cervical cancer classification using machine learning (ML) and deep learning (DL) has been extensively studied to enhance the conventional diagnostic process. Robust classification results were achieved through the pre-segmented imagery in most current investigations. Conversely, cellular grouping makes reliable cervical cellular segmentation difficult. Additionally, the deep learning methods used in the existing works perform poorly on a multiclass classification when the data distribution is skewed, which is common in the cervical cancer dataset. To mitigate these restrictions in cervical cancer research, this proposed work uses a combination of four different deep-learning methods in various phases of this research. The proposed work is segregated into five phases: pre-processing, data augmentation, segmentation, feature extraction, and classification. Contrast maximization is performed in the pre-processing phase, and the images are augmented using Multi-modal Generative Adversarial Networks (m-GAN) in the second phase. In the third phase, cervical cancer images are segmented using the Seg-UNet model, which is forwarded to the feature extraction phase that employs denoising autoencoders. Finally, the classification is implemented using the Dense CapsNet model and applied to the SIPaKMeD dataset to categorize between normal, abnormal, and benign classes. The proposed system achieves an accuracy of 99.65%, which is higher than the other works in the literature.
AbstractList	Cervical cancer is one of the deadly diseases that affects women, which requires periodic examinations to identify and treat any cancerous tumors at a preliminary stage. The most prevalent examination tool for cervical cancer prompt identification is the cervical smear (Pap smear) testing; however, due to human negligence, this examination method has an elevated probability of negative findings. Cervical cancer classification using machine learning (ML) and deep learning (DL) has been extensively studied to enhance the conventional diagnostic process. Robust classification results were achieved through the pre-segmented imagery in most current investigations. Conversely, cellular grouping makes reliable cervical cellular segmentation difficult. Additionally, the deep learning methods used in the existing works perform poorly on a multiclass classification when the data distribution is skewed, which is common in the cervical cancer dataset. To mitigate these restrictions in cervical cancer research, this proposed work uses a combination of four different deep-learning methods in various phases of this research. The proposed work is segregated into five phases: pre-processing, data augmentation, segmentation, feature extraction, and classification. Contrast maximization is performed in the pre-processing phase, and the images are augmented using Multi-modal Generative Adversarial Networks (m-GAN) in the second phase. In the third phase, cervical cancer images are segmented using the Seg-UNet model, which is forwarded to the feature extraction phase that employs denoising autoencoders. Finally, the classification is implemented using the Dense CapsNet model and applied to the SIPaKMeD dataset to categorize between normal, abnormal, and benign classes. The proposed system achieves an accuracy of 99.65%, which is higher than the other works in the literature. Cervical cancer is one of the deadly diseases that affects women, which requires periodic examinations to identify and treat any cancerous tumors at a preliminary stage. The most prevalent examination tool for cervical cancer prompt identification is the cervical smear (Pap smear) testing; however, due to human negligence, this examination method has an elevated probability of negative findings. Cervical cancer classification using machine learning (ML) and deep learning (DL) has been extensively studied to enhance the conventional diagnostic process. Robust classification results were achieved through the pre-segmented imagery in most current investigations. Conversely, cellular grouping makes reliable cervical cellular segmentation difficult. Additionally, the deep learning methods used in the existing works perform poorly on a multiclass classification when the data distribution is skewed, which is common in the cervical cancer dataset. To mitigate these restrictions in cervical cancer research, this proposed work uses a combination of four different deep-learning methods in various phases of this research. The proposed work is segregated into five phases: pre-processing, data augmentation, segmentation, feature extraction, and classification. Contrast maximization is performed in the pre-processing phase, and the images are augmented using Multi-modal Generative Adversarial Networks (m-GAN) in the second phase. In the third phase, cervical cancer images are segmented using the Seg-UNet model, which is forwarded to the feature extraction phase that employs denoising autoencoders. Finally, the classification is implemented using the Dense CapsNet model and applied to the SIPaKMeD dataset to categorize between normal, abnormal, and benign classes. The proposed system achieves an accuracy of 99.65%, which is higher than the other works in the literature.Cervical cancer is one of the deadly diseases that affects women, which requires periodic examinations to identify and treat any cancerous tumors at a preliminary stage. The most prevalent examination tool for cervical cancer prompt identification is the cervical smear (Pap smear) testing; however, due to human negligence, this examination method has an elevated probability of negative findings. Cervical cancer classification using machine learning (ML) and deep learning (DL) has been extensively studied to enhance the conventional diagnostic process. Robust classification results were achieved through the pre-segmented imagery in most current investigations. Conversely, cellular grouping makes reliable cervical cellular segmentation difficult. Additionally, the deep learning methods used in the existing works perform poorly on a multiclass classification when the data distribution is skewed, which is common in the cervical cancer dataset. To mitigate these restrictions in cervical cancer research, this proposed work uses a combination of four different deep-learning methods in various phases of this research. The proposed work is segregated into five phases: pre-processing, data augmentation, segmentation, feature extraction, and classification. Contrast maximization is performed in the pre-processing phase, and the images are augmented using Multi-modal Generative Adversarial Networks (m-GAN) in the second phase. In the third phase, cervical cancer images are segmented using the Seg-UNet model, which is forwarded to the feature extraction phase that employs denoising autoencoders. Finally, the classification is implemented using the Dense CapsNet model and applied to the SIPaKMeD dataset to categorize between normal, abnormal, and benign classes. The proposed system achieves an accuracy of 99.65%, which is higher than the other works in the literature. Abstract Cervical cancer is one of the deadly diseases that affects women, which requires periodic examinations to identify and treat any cancerous tumors at a preliminary stage. The most prevalent examination tool for cervical cancer prompt identification is the cervical smear (Pap smear) testing; however, due to human negligence, this examination method has an elevated probability of negative findings. Cervical cancer classification using machine learning (ML) and deep learning (DL) has been extensively studied to enhance the conventional diagnostic process. Robust classification results were achieved through the pre-segmented imagery in most current investigations. Conversely, cellular grouping makes reliable cervical cellular segmentation difficult. Additionally, the deep learning methods used in the existing works perform poorly on a multiclass classification when the data distribution is skewed, which is common in the cervical cancer dataset. To mitigate these restrictions in cervical cancer research, this proposed work uses a combination of four different deep-learning methods in various phases of this research. The proposed work is segregated into five phases: pre-processing, data augmentation, segmentation, feature extraction, and classification. Contrast maximization is performed in the pre-processing phase, and the images are augmented using Multi-modal Generative Adversarial Networks (m-GAN) in the second phase. In the third phase, cervical cancer images are segmented using the Seg-UNet model, which is forwarded to the feature extraction phase that employs denoising autoencoders. Finally, the classification is implemented using the Dense CapsNet model and applied to the SIPaKMeD dataset to categorize between normal, abnormal, and benign classes. The proposed system achieves an accuracy of 99.65%, which is higher than the other works in the literature.
ArticleNumber	31764
Author	Innab, Nisreen Ferrara, Massimiliano Ghoneim, Mohamed E. Yang, Hui Aydi, Walid
Author_xml	– sequence: 1 givenname: Hui surname: Yang fullname: Yang, Hui email: huiyangscientif@outlook.com organization: Department of Critical Medicine, Baoshan People’s Hospital – sequence: 2 givenname: Walid surname: Aydi fullname: Aydi, Walid organization: Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Laboratory of Electronics & Information Technologies, Sfax University – sequence: 3 givenname: Nisreen surname: Innab fullname: Innab, Nisreen organization: Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University – sequence: 4 givenname: Mohamed E. surname: Ghoneim fullname: Ghoneim, Mohamed E. organization: Faculty of Computers and Artificial Intelligence, Damietta University, Mathematics Department, Faculty of Sciences, Umm Al-Qura University – sequence: 5 givenname: Massimiliano surname: Ferrara fullname: Ferrara, Massimiliano email: massimiliano.ferrara@unirc.it organization: Decisions LAB, Department of Law, Economics and Human Sciences, University Mediterranea of Reggio Calabria
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Keywords	Deep learning Dense CapsNet Segmentation Autoencoders Cervical cancer
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Snippet	Cervical cancer is one of the deadly diseases that affects women, which requires periodic examinations to identify and treat any cancerous tumors at a... Abstract Cervical cancer is one of the deadly diseases that affects women, which requires periodic examinations to identify and treat any cancerous tumors at a...
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StartPage	31764
SubjectTerms	692/699/75 692/700/228 Algorithms Autoencoders Cervical cancer Classification Deep Learning Dense CapsNet Female Humanities and Social Sciences Humans Image processing Image Processing, Computer-Assisted - methods Machine Learning Medical research multidisciplinary Negligence Neural Networks, Computer Probability learning Science Science (multidisciplinary) Segmentation Uterine Cervical Neoplasms - classification Uterine Cervical Neoplasms - diagnosis Uterine Cervical Neoplasms - pathology
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Title	Classification of cervical cancer using Dense CapsNet with Seg-UNet and denoising autoencoders
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Volume	14
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