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Tan SL, Selvachandran G, Ding W, Paramesran R, Kotecha K. Cervical Cancer Classification From Pap Smear Images Using Deep Convolutional Neural Network Models. Interdiscip Sci 2024; 16:16-38. [PMID: 37962777 PMCID: PMC10881721 DOI: 10.1007/s12539-023-00589-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 11/15/2023]
Abstract
As one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional classification algorithms used for detection of cervical cancer often require cell segmentation and feature extraction techniques, while convolutional neural network (CNN) models demand a large dataset to mitigate over-fitting and poor generalization problems. To this end, this study aims to develop deep learning models for automated cervical cancer detection that do not rely on segmentation methods or custom features. Due to limited data availability, transfer learning was employed with pre-trained CNN models to directly operate on Pap smear images for a seven-class classification task. Thorough evaluation and comparison of 13 pre-trained deep CNN models were performed using the publicly available Herlev dataset and the Keras package in Google Collaboratory. In terms of accuracy and performance, DenseNet-201 is the best-performing model. The pre-trained CNN models studied in this paper produced good experimental results and required little computing time.
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Affiliation(s)
- Sher Lyn Tan
- Institute of Actuarial Science and Data Analytics, UCSI University, Jalan Menara Gading, Cheras, 56000, Kuala Lumpur, Malaysia
| | - Ganeshsree Selvachandran
- School of Business, Monash University Malaysia, Jalan Lagoon Selatan, Bandar Sunway, 47500, Subang Jaya, Malaysia.
- Symbiosis Institute of Technology, Symbiosis International University, Pune, 412115, Maharashtra, India.
| | - Weiping Ding
- School of Information Science and Technology, Nantong University, Nantong, 226019, China.
| | - Raveendran Paramesran
- School of Information Technology, Monash University Malaysia, Bandar Sunway, 47500, Subang Jaya, Malaysia
- Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia
| | - Ketan Kotecha
- Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Symbiosis Institute of Technology, Pune, 412115, India
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Chen X, Aljrees T, Umer M, Saidani O, Almuqren L, Mzoughi O, Ishaq A, Ashraf I. Cervical cancer detection using K nearest neighbor imputer and stacked ensemble learningmodel. Digit Health 2023; 9:20552076231203802. [PMID: 37799501 PMCID: PMC10548812 DOI: 10.1177/20552076231203802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 09/08/2023] [Indexed: 10/07/2023] Open
Abstract
Objective Cervical cancer stands as a leading cause of mortality among women in developing nations. To ensure the reduction of its adverse consequences, the primary protocols to be adhered to involve early detection and treatment under the guidance of expert medical professionals. An effective approach for identifying this form of malignancy involves the examination of Pap smear images. However, in the context of automating cervical cancer detection, many of the existing datasets frequently exhibit missing data points, a factor that can substantially impact the effectiveness of machine learning models. Methods In response to these hurdles, this research introduces an automated system designed to predict cervical cancer with a dual focus: adeptly managing missing data while attaining remarkable accuracy. The system's core is built upon a stacked ensemble voting classifier model, which amalgamates three distinct machine learning models, all harmoniously integrated with the KNN Imputer to address the issue of missing values. Results The model put forth attains an accuracy of 99.41%, precision of 97.63%, recall of 95.96%, and an F1 score of 96.76% when incorporating the KNN imputation method. The investigation conducts a comparative analysis, contrasting the performance of this model with seven alternative machine learning algorithms in two scenarios: one where missing values are eliminated, and another employing KNN imputation. This study offers validation of the effectiveness of the proposed model in comparison to current state-of-the-art methodologies. Conclusions This research delves into the challenge of handling missing data in the dataset utilized for cervical cancer detection. The findings have the potential to assist healthcare professionals in achieving early detection and enhancing the quality of care provided to individuals affected by cervical cancer.
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Affiliation(s)
- Xiaoyuan Chen
- Huzhou Key Laboratory of Green Energy Materials and Battery Cascade Utilization, School of Intelligent Manufacturing, Huzhou College, Huzhou, P.R. China
| | - Turki Aljrees
- Department College of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin, Saudi Arabia
| | - Muhammad Umer
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Oumaima Saidani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Latifah Almuqren
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Olfa Mzoughi
- Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Aflaj, Saudi Arabia
| | - Abid Ishaq
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Imran Ashraf
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, South Korea
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Wang W, Tian Y, Xu Y, Zhang XX, Li YS, Zhao SF, Bai YH. 3cDe-Net: a cervical cancer cell detection network based on an improved backbone network and multiscale feature fusion. BMC Med Imaging 2022; 22:130. [PMID: 35870877 PMCID: PMC9308346 DOI: 10.1186/s12880-022-00852-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 07/05/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Cervical cancer cell detection is an essential means of cervical cancer screening. However, for thin-prep cytology test (TCT)-based images, the detection accuracies of traditional computer-aided detection algorithms are typically low due to the overlapping of cells with blurred cytoplasmic boundaries. Some typical deep learning-based detection methods, e.g., ResNets and Inception-V3, are not always efficient for cervical images due to the differences between cervical cancer cell images and natural images. As a result, these traditional networks are difficult to directly apply to the clinical practice of cervical cancer screening. METHOD We propose a cervical cancer cell detection network (3cDe-Net) based on an improved backbone network and multiscale feature fusion; the proposed network consists of the backbone network and a detection head. In the backbone network, a dilated convolution and a group convolution are introduced to improve the resolution and expression ability of the model. In the detection head, multiscale features are obtained based on a feature pyramid fusion network to ensure the accurate capture of small cells; then, based on the Faster region-based convolutional neural network (R-CNN), adaptive cervical cancer cell anchors are generated via unsupervised clustering. Furthermore, a new balanced L1-based loss function is defined, which reduces the unbalanced sample contribution loss. RESULT Baselines including ResNet-50, ResNet-101, Inception-v3, ResNet-152 and the feature concatenation network are used on two different datasets (the Data-T and Herlev datasets), and the final quantitative results show the effectiveness of the proposed dilated convolution ResNet (DC-ResNet) backbone network. Furthermore, experiments conducted on both datasets show that the proposed 3cDe-Net, based on the optimal anchors, the defined new loss function, and DC-ResNet, outperforms existing methods and achieves a mean average precision (mAP) of 50.4%. By performing a horizontal comparison of the cells on an image, the category and location information of cancer cells can be obtained concurrently. CONCLUSION The proposed 3cDe-Net can detect cancer cells and their locations on multicell pictures. The model directly processes and analyses samples at the picture level rather than at the cellular level, which is more efficient. In clinical settings, the mechanical workloads of doctors can be reduced, and their focus can be placed on higher-level review work.
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Affiliation(s)
- Wei Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gynecologic Oncology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
| | - Yun Tian
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China.
| | - Yang Xu
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
| | - Xiao-Xuan Zhang
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
| | - Yan-Song Li
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
| | - Shi-Feng Zhao
- School of Artificial Intelligence, Beijing Normal University, Beijing, 100875, China
| | - Yan-Hua Bai
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Pathology, Peking University Cancer Hospital and Institute, Beijing, 100142, China
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Prabitha VG, Suchetha S, Jayanthi JL, Baiju KV, Rema P, Anuraj K, Mathews A, Sebastian P, Subhash N. Detection of cervical lesions by multivariate analysis of diffuse reflectance spectra: a clinical study. Lasers Med Sci 2015; 31:67-75. [PMID: 26521184 DOI: 10.1007/s10103-015-1829-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Accepted: 10/21/2015] [Indexed: 11/28/2022]
Abstract
Diffuse reflectance (DR) spectroscopy is a non-invasive, real-time, and cost-effective tool for early detection of malignant changes in squamous epithelial tissues. The present study aims to evaluate the diagnostic power of diffuse reflectance spectroscopy for non-invasive discrimination of cervical lesions in vivo. A clinical trial was carried out on 48 sites in 34 patients by recording DR spectra using a point-monitoring device with white light illumination. The acquired data were analyzed and classified using multivariate statistical analysis based on principal component analysis (PCA) and linear discriminant analysis (LDA). Diagnostic accuracies were validated using random number generators. The receiver operating characteristic (ROC) curves were plotted for evaluating the discriminating power of the proposed statistical technique. An algorithm was developed and used to classify non-diseased (normal) from diseased sites (abnormal) with a sensitivity of 72 % and specificity of 87 %. While low-grade squamous intraepithelial lesion (LSIL) could be discriminated from normal with a sensitivity of 56 % and specificity of 80 %, and high-grade squamous intraepithelial lesion (HSIL) from normal with a sensitivity of 89 % and specificity of 97 %, LSIL could be discriminated from HSIL with 100 % sensitivity and specificity. The areas under the ROC curves were 0.993 (95 % confidence interval (CI) 0.0 to 1) and 1 (95 % CI 1) for the discrimination of HSIL from normal and HSIL from LSIL, respectively. The results of the study show that DR spectroscopy could be used along with multivariate analytical techniques as a non-invasive technique to monitor cervical disease status in real time.
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Affiliation(s)
- Vasumathi Gopala Prabitha
- Biophotonics Laboratory, Centre for Earth Science Studies, Akkulam, Thiruvananthapuram, 695 031, Kerala, India
| | - Sambasivan Suchetha
- Regional Cancer Centre, Medical College P.O., Thiruvananthapuram, 695 011, Kerala, India
| | | | | | - Prabhakaran Rema
- Regional Cancer Centre, Medical College P.O., Thiruvananthapuram, 695 011, Kerala, India
| | - Koyippurath Anuraj
- Biophotonics Laboratory, Centre for Earth Science Studies, Akkulam, Thiruvananthapuram, 695 031, Kerala, India
| | - Anita Mathews
- Regional Cancer Centre, Medical College P.O., Thiruvananthapuram, 695 011, Kerala, India
| | - Paul Sebastian
- Regional Cancer Centre, Medical College P.O., Thiruvananthapuram, 695 011, Kerala, India
| | - Narayanan Subhash
- Biophotonics Laboratory, Centre for Earth Science Studies, Akkulam, Thiruvananthapuram, 695 031, Kerala, India. .,Sascan Meditech Pvt Ltd., CIME, BMS College of Engineering, Basavanagudi, Bull Temple Road, Bangalore, 560019, India.
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Duangkaew P, Tapaneeyakorn S, Apiwat C, Dharakul T, Laiwejpithaya S, Kanatharana P, Laocharoensuk R. Ultrasensitive electrochemical immunosensor based on dual signal amplification process for p16(INK4a) cervical cancer detection in clinical samples. Biosens Bioelectron 2015. [PMID: 26201985 DOI: 10.1016/j.bios.2015.07.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The p16(INK4a) (p16) is a cyclin-dependent kinase inhibitor, which has been evaluated in several studies as a diagnostic marker of cervical cancer. Immunostaining using p16 specific antibody has confirmed an over-expression of p16 protein in cervical cancer cells and its association with disease progression. This article reports an ultrasensitive electrochemical immunosensor for specific detection of p16 and demonstrates its performance for detection of solubilized p16 protein in cell lysates obtained from patients. Sandwich-based immunoreaction couple with double signal amplification strategy based on catalytic enlargement of particle tag was used for high sensitivity and specificity. The conditions were optimized to create an immunoassay protocol. Disposable screen-printed electrode modified with capture antibodies (Ab1) was selected for further implementation towards point-of-care diagnostics. Small gold nanoparticles (15 nm diameter) conjugated with detection antibodies (Ab2) were found to better serve as a detection label due to limited interference with antigen-antibody interaction. Double signal enhancement was performed by sequential depositions of gold and silver layers. This gave the sensitivity of 1.78 μA mL(ng GST-p16)(-1) cm(-2) and detection limit of 1.3 ng mL(-1) for GST-p16 protein which is equivalent to 0.49 ng mL(-1) for p16 protein and 28 cells for HeLa cervical cancer cells. In addition to purified protein, the proposed immunosensor effectively detected elevated p16 level in cervical swab samples obtained from 10 patients with positive result from standard Pap smear test, indicating that an electrochemical immunosensors hold an excellent promise for detection of cervical cancer in clinical setting.
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Affiliation(s)
- Pattasuda Duangkaew
- Nanostructures and Functional Assembly Laboratory, National Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand
| | - Satita Tapaneeyakorn
- Nanomolecular Target Discovery Laboratory, National Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand
| | - Chayachon Apiwat
- Nanomolecular Target Discovery Laboratory, National Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand
| | - Tararaj Dharakul
- Nanomolecular Target Discovery Laboratory, National Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand; Department of Immunology and Department of Obstetrics and Gynaecology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Somsak Laiwejpithaya
- Department of Immunology and Department of Obstetrics and Gynaecology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Proespichaya Kanatharana
- Trace Analysis and Biosensor Research Center, Department of Chemistry, Faculty of Science, Prince of Songkla University, Songkhla, Thailand
| | - Rawiwan Laocharoensuk
- Nanostructures and Functional Assembly Laboratory, National Nanotechnology Center (NANOTEC), National Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand.
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Acosta-Mesa HG, Rechy-Ramírez F, Mezura-Montes E, Cruz-Ramírez N, Hernández Jiménez R. Application of time series discretization using evolutionary programming for classification of precancerous cervical lesions. J Biomed Inform 2014; 49:73-83. [PMID: 24637143 DOI: 10.1016/j.jbi.2014.03.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2013] [Revised: 02/18/2014] [Accepted: 03/03/2014] [Indexed: 10/25/2022]
Abstract
In this work, we present a novel application of time series discretization using evolutionary programming for the classification of precancerous cervical lesions. The approach optimizes the number of intervals in which the length and amplitude of the time series should be compressed, preserving the important information for classification purposes. Using evolutionary programming, the search for a good discretization scheme is guided by a cost function which considers three criteria: the entropy regarding the classification, the complexity measured as the number of different strings needed to represent the complete data set, and the compression rate assessed as the length of the discrete representation. This discretization approach is evaluated using a time series data based on temporal patterns observed during a classical test used in cervical cancer detection; the classification accuracy reached by our method is compared with the well-known times series discretization algorithm SAX and the dimensionality reduction method PCA. Statistical analysis of the classification accuracy shows that the discrete representation is as efficient as the complete raw representation for the present application, reducing the dimensionality of the time series length by 97%. This representation is also very competitive in terms of classification accuracy when compared with similar approaches.
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Affiliation(s)
- Héctor-Gabriel Acosta-Mesa
- School of Physics and Artificial Intelligence, Department of Artificial Intelligence, Universidad Veracruzana, Sebastián Camacho # 5, 91000 Xalapa, Veracruz, Mexico.
| | - Fernando Rechy-Ramírez
- School of Physics and Artificial Intelligence, Department of Artificial Intelligence, Universidad Veracruzana, Sebastián Camacho # 5, 91000 Xalapa, Veracruz, Mexico.
| | - Efrén Mezura-Montes
- School of Physics and Artificial Intelligence, Department of Artificial Intelligence, Universidad Veracruzana, Sebastián Camacho # 5, 91000 Xalapa, Veracruz, Mexico.
| | - Nicandro Cruz-Ramírez
- School of Physics and Artificial Intelligence, Department of Artificial Intelligence, Universidad Veracruzana, Sebastián Camacho # 5, 91000 Xalapa, Veracruz, Mexico.
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Gibb RK, Martens MG. The impact of liquid-based cytology in decreasing the incidence of cervical cancer. Rev Obstet Gynecol 2011; 4:S2-S11. [PMID: 21617785 PMCID: PMC3101960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Major advances in screening have lowered the death rate from cervical cancer in the United States. One of the first major advances in cervical cancer screening was the Papanicolaou (Pap) test. The second major advance was liquid-based cytology (LBC). This review presents a wide range of data, discusses the strengths and weaknesses of the available information regarding Pap technologies, and reviews the meta-analyses, which have examined the differences in clinical performance. The review concludes with information on new and future developments to further decrease cervical cancer deaths.
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