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Yang T, Hu H, Li X, Meng Q, Lu H, Huang Q. An efficient Fusion-Purification Network for Cervical pap-smear image classification. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 251:108199. [PMID: 38728830 DOI: 10.1016/j.cmpb.2024.108199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/28/2024] [Accepted: 04/21/2024] [Indexed: 05/12/2024]
Abstract
BACKGROUND AND OBJECTIVES In cervical cell diagnostics, autonomous screening technology constitutes the foundation of automated diagnostic systems. Currently, numerous deep learning-based classification techniques have been successfully implemented in the analysis of cervical cell images, yielding favorable outcomes. Nevertheless, efficient discrimination of cervical cells continues to be challenging due to large intra-class and small inter-class variations. The key to dealing with this problem is to capture localized informative differences from cervical cell images and to represent discriminative features efficiently. Existing methods neglect the importance of global morphological information, resulting in inadequate feature representation capability. METHODS To address this limitation, we propose a novel cervical cell classification model that focuses on purified fusion information. Specifically, we first integrate the detailed texture information and morphological structure features, named cervical pathology information fusion. Second, in order to enhance the discrimination of cervical cell features and address the data redundancy and bias inherent after fusion, we design a cervical purification bottleneck module. This model strikes a balance between leveraging purified features and facilitating high-efficiency discrimination. Furthermore, we intend to unveil a more intricate cervical cell dataset: Cervical Cytopathology Image Dataset (CCID). RESULTS Extensive experiments on two real-world datasets show that our proposed model outperforms state-of-the-art cervical cell classification models. CONCLUSIONS The results show that our method can well help pathologists to accurately evaluate cervical smears.
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Affiliation(s)
- Tianjin Yang
- College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
| | - Hexuan Hu
- College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
| | - Xing Li
- College of information Science and Technology & College of Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, PR China.
| | - Qing Meng
- College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
| | - Hao Lu
- College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
| | - Qian Huang
- College of Computer and Information, Hohai University, Nanjing, 211100, PR China.
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Munshi RM. Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction. PLoS One 2024; 19:e0296107. [PMID: 38198475 PMCID: PMC10781159 DOI: 10.1371/journal.pone.0296107] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/06/2023] [Indexed: 01/12/2024] Open
Abstract
Cervical cancer remains a leading cause of female mortality, particularly in developing regions, underscoring the critical need for early detection and intervention guided by skilled medical professionals. While Pap smear images serve as valuable diagnostic tools, many available datasets for automated cervical cancer detection contain missing data, posing challenges for machine learning models' efficacy. To address these hurdles, this study presents an automated system adept at managing missing information using ADASYN characteristics, resulting in exceptional accuracy. The proposed methodology integrates a voting classifier model harnessing the predictive capacity of three distinct machine learning models. It further incorporates SVM Imputer and ADASYN up-sampled features to mitigate missing value concerns, while leveraging CNN-generated features to augment the model's capabilities. Notably, this model achieves remarkable performance metrics, boasting a 99.99% accuracy, precision, recall, and F1 score. A comprehensive comparative analysis evaluates the proposed model against various machine learning algorithms across four scenarios: original dataset usage, SVM imputation, ADASYN feature utilization, and CNN-generated features. Results indicate the superior efficacy of the proposed model over existing state-of-the-art techniques. This research not only introduces a novel approach but also offers actionable suggestions for refining automated cervical cancer detection systems. Its impact extends to benefiting medical practitioners by enabling earlier detection and improved patient care. Furthermore, the study's findings have substantial societal implications, potentially reducing the burden of cervical cancer through enhanced diagnostic accuracy and timely intervention.
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Affiliation(s)
- Raafat M. Munshi
- Department of Medical Laboratory Technology (MLT), Faculty of Applied Medical Sciences, King Abdulaziz University, Rabigh, Saudi Arabia
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Karamti H, Alharthi R, Anizi AA, Alhebshi RM, Eshmawi AA, Alsubai S, Umer M. Improving Prediction of Cervical Cancer Using KNN Imputed SMOTE Features and Multi-Model Ensemble Learning Approach. Cancers (Basel) 2023; 15:4412. [PMID: 37686692 PMCID: PMC10486648 DOI: 10.3390/cancers15174412] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/02/2023] [Accepted: 08/09/2023] [Indexed: 09/10/2023] Open
Abstract
Objective: Cervical cancer ranks among the top causes of death among females in developing countries. The most important procedures that should be followed to guarantee the minimizing of cervical cancer's aftereffects are early identification and treatment under the finest medical guidance. One of the best methods to find this sort of malignancy is by looking at a Pap smear image. For automated detection of cervical cancer, the available datasets often have missing values, which can significantly affect the performance of machine learning models. Methods: To address these challenges, this study proposes an automated system for predicting cervical cancer that efficiently handles missing values with SMOTE features to achieve high accuracy. The proposed system employs a stacked ensemble voting classifier model that combines three machine learning models, along with KNN Imputer and SMOTE up-sampled features for handling missing values. Results: The proposed model achieves 99.99% accuracy, 99.99% precision, 99.99% recall, and 99.99% F1 score when using KNN imputed SMOTE features. The study compares the performance of the proposed model with multiple other machine learning algorithms under four scenarios: with missing values removed, with KNN imputation, with SMOTE features, and with KNN imputed SMOTE features. The study validates the efficacy of the proposed model against existing state-of-the-art approaches. Conclusions: This study investigates the issue of missing values and class imbalance in the data collected for cervical cancer detection and might aid medical practitioners in timely detection and providing cervical cancer patients with better care.
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Affiliation(s)
- Hanen Karamti
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Raed Alharthi
- Department of Computer Science and Engineering, University of Hafr Al-Batin, Hafar Al-Batin 39524, Saudi Arabia;
| | - Amira Al Anizi
- Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Reemah M. Alhebshi
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Ala’ Abdulmajid Eshmawi
- Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah 23218, Saudi Arabia;
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia;
| | - Muhammad Umer
- Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
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4
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Mohsin SN, Gapizov A, Ekhator C, Ain NU, Ahmad S, Khan M, Barker C, Hussain M, Malineni J, Ramadhan A, Halappa Nagaraj R. The Role of Artificial Intelligence in Prediction, Risk Stratification, and Personalized Treatment Planning for Congenital Heart Diseases. Cureus 2023; 15:e44374. [PMID: 37664359 PMCID: PMC10469091 DOI: 10.7759/cureus.44374] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2023] [Indexed: 09/05/2023] Open
Abstract
This narrative review delves into the potential of artificial intelligence (AI) in predicting, stratifying risk, and personalizing treatment planning for congenital heart disease (CHD). CHD is a complex condition that affects individuals across various age groups. The review highlights the challenges in predicting risks, planning treatments, and prognosticating long-term outcomes due to CHD's multifaceted nature, limited data, ethical concerns, and individual variabilities. AI, with its ability to analyze extensive data sets, presents a promising solution. The review emphasizes the need for larger, diverse datasets, the integration of various data sources, and the analysis of longitudinal data. Prospective validation in real-world clinical settings, interpretability, and the importance of human clinical expertise are also underscored. The ethical considerations surrounding privacy, consent, bias, monitoring, and human oversight are examined. AI's implications include improved patient outcomes, cost-effectiveness, and real-time decision support. The review aims to provide a comprehensive understanding of AI's potential for revolutionizing CHD management and highlights the significance of collaboration and transparency to address challenges and limitations.
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Affiliation(s)
| | | | - Chukwuyem Ekhator
- Neuro-Oncology, New York Institute of Technology, College of Osteopathic Medicine, Old Westbury, USA
| | - Noor U Ain
- Medicine, Mayo Hospital, Lahore, PAK
- Medicine, King Edward Medical University, Lahore, PAK
| | | | - Mavra Khan
- Medicine and Surgery, Mayo Hospital, Lahore , PAK
| | - Chad Barker
- Public Health, University of South Florida, Tampa, USA
| | | | - Jahnavi Malineni
- Medicine and Surgery, Maharajah's Institute of Medical Sciences, Vizianagaram, IND
| | - Afif Ramadhan
- Medicine, Universal Scientific Education and Research Network (USERN), Yogyakarta, IDN
- Medicine, Faculty of Medicine, Public Health, and Nursing, Gadjah Mada University, Yogyakarta, IDN
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Hamdi M, Senan EM, Awaji B, Olayah F, Jadhav ME, Alalayah KM. Analysis of WSI Images by Hybrid Systems with Fusion Features for Early Diagnosis of Cervical Cancer. Diagnostics (Basel) 2023; 13:2538. [PMID: 37568901 PMCID: PMC10416962 DOI: 10.3390/diagnostics13152538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 07/22/2023] [Accepted: 07/25/2023] [Indexed: 08/13/2023] Open
Abstract
Cervical cancer is one of the most common types of malignant tumors in women. In addition, it causes death in the latter stages. Squamous cell carcinoma is the most common and aggressive form of cervical cancer and must be diagnosed early before it progresses to a dangerous stage. Liquid-based cytology (LBC) swabs are best and most commonly used for cervical cancer screening and are converted from glass slides to whole-slide images (WSIs) for computer-assisted analysis. Manual diagnosis by microscopes is limited and prone to manual errors, and tracking all cells is difficult. Therefore, the development of computational techniques is important as diagnosing many samples can be done automatically, quickly, and efficiently, which is beneficial for medical laboratories and medical professionals. This study aims to develop automated WSI image analysis models for early diagnosis of a cervical squamous cell dataset. Several systems have been designed to analyze WSI images and accurately distinguish cervical cancer progression. For all proposed systems, the WSI images were optimized to show the contrast of edges of the low-contrast cells. Then, the cells to be analyzed were segmented and isolated from the rest of the image using the Active Contour Algorithm (ACA). WSI images were diagnosed by a hybrid method between deep learning (ResNet50, VGG19 and GoogLeNet), Random Forest (RF), and Support Vector Machine (SVM) algorithms based on the ACA algorithm. Another hybrid method for diagnosing WSI images by RF and SVM algorithms is based on fused features of deep-learning (DL) models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet). It is concluded from the systems' performance that the DL models' combined features help significantly improve the performance of the RF and SVM networks. The novelty of this research is the hybrid method that combines the features extracted from deep-learning models (ResNet50-VGG19, VGG19-GoogLeNet, and ResNet50-GoogLeNet) with RF and SVM algorithms for diagnosing WSI images. The results demonstrate that the combined features from deep-learning models significantly improve the performance of RF and SVM. The RF network with fused features of ResNet50-VGG19 achieved an AUC of 98.75%, a sensitivity of 97.4%, an accuracy of 99%, a precision of 99.6%, and a specificity of 99.2%.
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Affiliation(s)
- Mohammed Hamdi
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia;
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana’a, Yemen
| | - Bakri Awaji
- Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia;
| | - Fekry Olayah
- Department of Information Systems, College of Computer Science and Information Systems, Najran University, Najran 66462, Saudi Arabia;
| | - Mukti E. Jadhav
- Shri Shivaji Science & Arts College, Chikhli Dist., Buldana 443112, India;
| | - Khaled M. Alalayah
- Department of Computer Science, College of Science and Arts, Najran University, Sharurah 68341, Saudi Arabia;
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Mustafa WA, Ismail S, Mokhtar FS, Alquran H, Al-Issa Y. Cervical Cancer Detection Techniques: A Chronological Review. Diagnostics (Basel) 2023; 13:diagnostics13101763. [PMID: 37238248 DOI: 10.3390/diagnostics13101763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 05/12/2023] [Accepted: 05/15/2023] [Indexed: 05/28/2023] Open
Abstract
Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included "(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)". Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease's burden on women worldwide.
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Affiliation(s)
- Wan Azani Mustafa
- Faculty of Electrical Engineering Technology, Campus Pauh Putra, Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia
- Advanced Computing (AdvComp), Centre of Excellence (CoE), Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia
| | - Shahrina Ismail
- Faculty of Science and Technology, Universiti Sains Islam Malaysia (USIM), Bandar Baru Nilai 71800, Negeri Sembilan, Malaysia
| | - Fahirah Syaliza Mokhtar
- Faculty of Business, Economy and Social Development, Universiti Malaysia Terengganu, Kuala Nerus 21300, Terengganu, Malaysia
| | - Hiam Alquran
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, 556, Irbid 21163, Jordan
| | - Yazan Al-Issa
- Department of Computer Engineering, Yarmouk University, Irbid 22110, Jordan
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Talukder MA, Hasan KF, Islam MM, Uddin MA, Akhter A, Yousuf MA, Alharbi F, Moni MA. A dependable hybrid machine learning model for network intrusion detection. JOURNAL OF INFORMATION SECURITY AND APPLICATIONS 2023. [DOI: 10.1016/j.jisa.2022.103405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Alias NA, Mustafa WA, Jamlos MA, Alquran H, Hanafi HF, Ismail S, Rahman KSA. Pap Smear Images Classification Using Machine Learning: A Literature Matrix. Diagnostics (Basel) 2022; 12:diagnostics12122900. [PMID: 36552907 PMCID: PMC9776577 DOI: 10.3390/diagnostics12122900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/03/2022] [Accepted: 11/19/2022] [Indexed: 11/24/2022] Open
Abstract
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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Affiliation(s)
- Nur Ain Alias
- Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar 02100, Perlis, Malaysia
| | - Wan Azani Mustafa
- Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar 02100, Perlis, Malaysia
- Advanced Computing (AdvCOMP), Centre of Excellence, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, Arau 02600, Perlis, Malaysia
- Correspondence:
| | - Mohd Aminudin Jamlos
- Faculty of Electronic Engineering & Technology, Universiti Malaysia Perlis, UniCITI Alam Campus, Sungai Chuchuh, Padang Besar 02100, Perlis, Malaysia
| | - Hiam Alquran
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan
| | - Hafizul Fahri Hanafi
- Department of Computing, Faculty of Art, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjong Malim 35900, Perak, Malaysia
| | - Shahrina Ismail
- Faculty of Science and Technology, Universiti Sains Islam Malaysia (USIM), Bandar Baru Nilai 71800, Negeri Sembilan, Malaysia
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Allahqoli L, Laganà AS, Mazidimoradi A, Salehiniya H, Günther V, Chiantera V, Karimi Goghari S, Ghiasvand MM, Rahmani A, Momenimovahed Z, Alkatout I. Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review. Diagnostics (Basel) 2022; 12:2771. [PMID: 36428831 PMCID: PMC9689914 DOI: 10.3390/diagnostics12112771] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/06/2022] [Accepted: 11/10/2022] [Indexed: 11/16/2022] Open
Abstract
OBJECTIVE The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. MATERIALS AND METHODS Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. RESULTS The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80-100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9-98.22% and 51.8-96.2%, respectively. CONCLUSION The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.
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Affiliation(s)
- Leila Allahqoli
- Midwifery Department, Ministry of Health and Medical Education, Tehran 1467664961, Iran
| | - Antonio Simone Laganà
- Unit of Gynecologic Oncology, ARNAS “Civico-Di Cristina-Benfratelli”, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, 90127 Palermo, Italy
| | - Afrooz Mazidimoradi
- Neyriz Public Health Clinic, Shiraz University of Medical Sciences, Shiraz 7134814336, Iran
| | - Hamid Salehiniya
- Social Determinants of Health Research Center, Birjand University of Medical Sciences, Birjand 9717853577, Iran
| | - Veronika Günther
- University Hospitals Schleswig-Holstein, Campus Kiel, Kiel School of Gynaecological Endoscopy, Arnold-Heller-Str. 3, Haus 24, 24105 Kiel, Germany
| | - Vito Chiantera
- Unit of Gynecologic Oncology, ARNAS “Civico-Di Cristina-Benfratelli”, Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties (PROMISE), University of Palermo, 90127 Palermo, Italy
| | - Shirin Karimi Goghari
- School of Industrial and Systems Engineering, Tarbiat Modares University (TMU), Tehran 1411713114, Iran
| | - Mohammad Matin Ghiasvand
- Department of Computer Engineering, Amirkabir University of Technology (AUT), Tehran 1591634311, Iran
| | - Azam Rahmani
- Nursing and Midwifery Care Research Centre, School of Nursing and Midwifery, Tehran University of Medical Sciences, Tehran 141973317, Iran
| | - Zohre Momenimovahed
- Reproductive Health Department, Qom University of Medical Sciences, Qom 3716993456, Iran
| | - Ibrahim Alkatout
- University Hospitals Schleswig-Holstein, Campus Kiel, Kiel School of Gynaecological Endoscopy, Arnold-Heller-Str. 3, Haus 24, 24105 Kiel, Germany
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Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4688327. [PMID: 35572826 PMCID: PMC9095387 DOI: 10.1155/2022/4688327] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 03/25/2022] [Accepted: 03/26/2022] [Indexed: 11/18/2022]
Abstract
Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about 37% of cancer cases. The Pap smear examination is a standard screening procedure for the initial screening of cervical cancer. However, this manual test procedure generates many false-positive outcomes due to individual errors. Various researchers have extensively investigated machine learning (ML) methods for classifying cervical Pap cells to enhance manual testing. The random forest method is the most popular method for anticipating features from a high-dimensional cancer image dataset. However, the random forest method can get too slow and inefficient for real-time forecasts when too many decision trees are used. This research proposed an efficient feature selection and prediction model for cervical cancer datasets using Boruta analysis and SVM method to deal with this challenge. A Boruta analysis method is used. It is improved from of random forest method and mainly discovers feature subsets from the data source that are significant to assigned classification activity. The proposed model's primary aim is to determine the importance of cervical cancer screening factors for classifying high-risk patients depending on the findings. This research work analyses cervical cancer and various risk factors to help detect cervical cancer. The proposed model Boruta with SVM and various popular ML models are implemented using Python and various performance measuring parameters, i.e., accuracy, precision, F1–Score, and recall. However, the proposed Boruta analysis with SVM performs outstanding over existing methods.
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AIN ALIAS NUR, AZANI MUSTAFA WAN, AMINUDIN JAMLOS MOHD, ALKHAYYAT AHMED, SHAKIR AB RAHMAN KHAIRUL, Q. MALIK RAMI. Improvement method for cervical cancer detection: A comparative analysis. Oncol Res 2021. [DOI: 10.32604/or.2022.025897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023] Open
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