Auxiliary classification of cervical cells based on multi-domain hybrid deep learning framework

Zhang, Chuanwang; Jia, Dongfang; Li, Ziqi; Wu, Nengkai

doi:10.1016/j.bspc.2022.103739

Cited by 12 publications

(5 citation statements)

References 48 publications

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“…Zhang et al [70] proposed a novel multi-domain hybrid deep learning framework (MDHDN) to classify cervical cells. It was the first time to apply cell spectrum for cervical cell classification.…”

Section: Hybrid Feature Fusion Based Identificationmentioning

confidence: 99%

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A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis

Jiang

Shen

et al. 2023

Preprint

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Cervical cancer is one of the most common cancers in daily life. Early detection and diagnosis can effectively help facilitate subsequent clinical treatment and management. With the growing advancement of artificial intelligence (AI) and deep learning (DL) techniques, an increasing number of computer-aided diagnosis (CAD) methods based on deep learning have been applied in cervical cytology screening. In this paper, we survey more than 70 publications since 2016 to provide a systematic and comprehensive review of DL-based cervical cytology screening. First, we provide a concise summary of the medical and biological knowledge pertaining to cervical cytology, since we hold a firm belief that a comprehensive biomedical understanding can significantly contribute to the development of CAD systems. Then, we collect a wide range of public cervical cytology datasets. Besides, image analysis approaches and applications including cervical cell identification, abnormal cell or area detection, cell region segmentation and cervical whole slide image diagnosis are summarized. Finally, we discuss the present obstacles and promising directions for future researches in automated cervical cytology screening.

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Section: Hybrid Feature Fusion Based Identificationmentioning

confidence: 99%

“…(70,197 single-cell images in 5 categories) 2-class, 5-class HUSTC (2-class): Acc = 99.52%, Sens = 98.90%, Spec = 98.12%, F1 = 99.71%; HUSTC (5-class): Acc = 81.88%, Sens = 79.58%, Spec = 94.97%, F1 = 79.10%; SIPaKMeD (2-class): Acc = 98.96%, Sens = 98.60%, Spec = 99.21%, F1 = 98.72%; SIPaKMeD (5-class): Acc = 98.67%, Sens = 98.65%, Spec = 99.67%, F1 = 98.67%…”

mentioning

confidence: 99%

A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis

Jiang

Shen

et al. 2023

Preprint

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“…An Additional classification of cervical cells based on a multi-domain hybrid deep learning architecture was proposed by Chuanwang Zhang et al [9]. By attempting for the first time to classify cervical cells using the multidomain hybrid deep learning framework (MDHDN), they address the restrictions.…”

Section: Contribution Of the Proposed Systemmentioning

confidence: 99%

A Versatile Detection of Cervical Cancer with i-WFCM and Deep Learning based RBM Classification

Haridas

Jayamalar

2023

JMC

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One of the most common and curable types of cancer in women is cervical cancer, a common chronic condition. Pap smear images is a common way for screening the cervical cancer. It does not present with symptoms until the disease has advanced stages, cervical cancer cannot be detected in its early stages. Because of this, accurate staging will make it easier to give the patient the right amount of treatment. In this paper proposes the Anisotropic Diffusion Filter has been used to improve the Pap smear image by removing noise and preserving the image's edges. The contrast of a Pap smear image has been enhanced using Histogram Equalization. The enhanced image has been segmented using Improved Weighted Fuzzy C-means clustering to make it easier to identify the effective features. As a result, the effective features are extracted from the segmented region and used by a Restricted Boltzmann Machine classifier based on Deep Learning to classify the cancer. The performance of the proposed cervical cancer detection system can be measured in terms of sensitivity, specificity, F-measure and accuracy. The performance measures for the proposed system can be achieves 95.3% accuracy, 88.6% specificity, 89.13% precision, 88.56% recall, and 89.7% F-measure respectively. Based on simulation results, the proposed method performs better than conventional methods such as RDVLNN, Random Forest (RF), Extreme Learning Machine (ELM), and Support Vector Machine (SVM) for detecting cervical cancer.

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“…However, if a large number of decision trees are utilised, the random forest approach might become too sluggish and ineffective for real-time predictions [ 10 ]. In addition, current classification approaches, such as deep learning (DL) or hand-crafted techniques, mostly rely on single detection structures and have high processing complexity and low accuracy [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

“…Prior to the development of DL, many of these tasks were regarded as being beyond the capabilities of computers, even in science fiction literature. However, DL methods are proposed to be able to compensate for the problem through computer-aided systems for cancer cell classification [ 8 , 12 , 13 , 17 , 24 , 26 , 27 , 28 , 29 , 30 ]. Hence, this study’s objective is to review the current development in technologies for cervical cell classification using machine learning.…”

Section: Introductionmentioning

confidence: 99%

Pap Smear Images Classification Using Machine Learning: A Literature Matrix

et al. 2022

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Cervical cancer is regularly diagnosed in women all over the world. This cancer is the seventh most frequent cancer globally and the fourth most prevalent cancer among women. Automated and higher accuracy of cervical cancer classification methods are needed for the early diagnosis of cancer. In addition, this study has proved that routine Pap smears could enhance clinical outcomes by facilitating the early diagnosis of cervical cancer. Liquid-based cytology (LBC)/Pap smears for advanced cervical screening is a highly effective precancerous cell detection technology based on cell image analysis, where cells are classed as normal or abnormal. Computer-aided systems in medical imaging have benefited greatly from extraordinary developments in artificial intelligence (AI) technology. However, resource and computational cost constraints prevent the widespread use of AI-based automation-assisted cervical cancer screening systems. Hence, this paper reviewed the related studies that have been done by previous researchers related to the automation of cervical cancer classification based on machine learning. The objective of this study is to systematically review and analyses the current research on the classification of the cervical using machine learning. The literature that has been reviewed is indexed by Scopus and Web of Science. As a result, for the published paper access until October 2022, this study assessed past approaches for cervical cell classification based on machine learning applications.

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Auxiliary classification of cervical cells based on multi-domain hybrid deep learning framework

Cited by 12 publications

References 48 publications

A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis

A Survey on Deep Learning-based Cervical Cytology Screening: from Cell Identification to Whole Slide Image Analysis

A Versatile Detection of Cervical Cancer with i-WFCM and Deep Learning based RBM Classification

Pap Smear Images Classification Using Machine Learning: A Literature Matrix

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