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Ullah S, Ou J, Xie Y, Tian W. Facial expression recognition (FER) survey: a vision, architectural elements, and future directions. PeerJ Comput Sci 2024; 10:e2024. [PMID: 38855254 PMCID: PMC11157619 DOI: 10.7717/peerj-cs.2024] [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: 01/31/2024] [Accepted: 04/08/2024] [Indexed: 06/11/2024]
Abstract
With the cutting-edge advancements in computer vision, facial expression recognition (FER) is an active research area due to its broad practical applications. It has been utilized in various fields, including education, advertising and marketing, entertainment and gaming, health, and transportation. The facial expression recognition-based systems are rapidly evolving due to new challenges, and significant research studies have been conducted on both basic and compound facial expressions of emotions; however, measuring emotions is challenging. Fueled by the recent advancements and challenges to the FER systems, in this article, we have discussed the basics of FER and architectural elements, FER applications and use-cases, FER-based global leading companies, interconnection between FER, Internet of Things (IoT) and Cloud computing, summarize open challenges in-depth to FER technologies, and future directions through utilizing Preferred Reporting Items for Systematic reviews and Meta Analyses Method (PRISMA). In the end, the conclusion and future thoughts are discussed. By overcoming the identified challenges and future directions in this research study, researchers will revolutionize the discipline of facial expression recognition in the future.
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Affiliation(s)
- Sana Ullah
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Jie Ou
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Yuanlun Xie
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Wenhong Tian
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
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2
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Fang M, Fu M, Liao B, Lei X, Wu FX. Deep integrated fusion of local and global features for cervical cell classification. Comput Biol Med 2024; 171:108153. [PMID: 38364660 DOI: 10.1016/j.compbiomed.2024.108153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 02/08/2024] [Accepted: 02/12/2024] [Indexed: 02/18/2024]
Abstract
Cervical cytology image classification is of great significance to the cervical cancer diagnosis and prognosis. Recently, convolutional neural network (CNN) and visual transformer have been adopted as two branches to learn the features for image classification by simply adding local and global features. However, such the simple addition may not be effective to integrate these features. In this study, we explore the synergy of local and global features for cytology images for classification tasks. Specifically, we design a Deep Integrated Feature Fusion (DIFF) block to synergize local and global features of cytology images from a CNN branch and a transformer branch. Our proposed method is evaluated on three cervical cell image datasets (SIPaKMeD, CRIC, Herlev) and another large blood cell dataset BCCD for several multi-class and binary classification tasks. Experimental results demonstrate the effectiveness of the proposed method in cervical cell classification, which could assist medical specialists to better diagnose cervical cancer.
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Affiliation(s)
- Ming Fang
- Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada
| | - Minghan Fu
- Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, 99 Longkun South Road, Haikou, 571158, Hainan, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, 620 West Chang'an Avenue, Xi'an, 710119, Shaanxi, China.
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada; Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada; Department of Computer Science, University of Saskatchewan, 57 Campus Drive, Saskatoon, S7N 5A9, SK, Canada.
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Khan A, Han S, Ilyas N, Lee YM, Lee B. CervixFormer: A Multi-scale swin transformer-Based cervical pap-Smear WSI classification framework. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107718. [PMID: 37451230 DOI: 10.1016/j.cmpb.2023.107718] [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: 01/02/2023] [Revised: 06/05/2023] [Accepted: 07/08/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND AND OBJECTIVES Cervical cancer affects around 0.5 million women per year, resulting in over 0.3 million fatalities. Therefore, repetitive screening for cervical cancer is of utmost importance. Computer-assisted diagnosis is key for scaling up cervical cancer screening. Current recognition algorithms, however, perform poorly on the whole-slide image (WSI) analysis, fail to generalize for different staining methods and on uneven distribution for subtype imaging, and provide sub-optimal clinical-level interpretations. Herein, we developed CervixFormer-an end-to-end, multi-scale swin transformer-based adversarial ensemble learning framework to assess pre-cancerous and cancer-specific cervical malignant lesions on WSIs. METHODS The proposed framework consists of (1) a self-attention generative adversarial network (SAGAN) for generating synthetic images during patch-level training to address the class imbalanced problems; (2) a multi-scale transformer-based ensemble learning method for cell identification at various stages, including atypical squamous cells (ASC) and atypical squamous cells of undetermined significance (ASCUS), which have not been demonstrated in previous studies; and (3) a fusion model for concatenating ensemble-based results and producing final outcomes. RESULTS In the evaluation, the proposed method is first evaluated on a private dataset of 717 annotated samples from six classes, obtaining a high recall and precision of 0.940 and 0.934, respectively, in roughly 1.2 minutes. To further examine the generalizability of CervixFormer, we evaluated it on four independent, publicly available datasets, namely, the CRIC cervix, Mendeley LBC, SIPaKMeD Pap Smear, and Cervix93 Extended Depth of Field image datasets. CervixFormer obtained a fairly better performance on two-, three-, four-, and six-class classification of smear- and cell-level datasets. For clinical interpretation, we used GradCAM to visualize a coarse localization map, highlighting important regions in the WSI. Notably, CervixFormer extracts feature mostly from the cell nucleus and partially from the cytoplasm. CONCLUSIONS In comparison with the existing state-of-the-art benchmark methods, the CervixFormer outperforms them in terms of recall, accuracy, and computing time.
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Affiliation(s)
- Anwar Khan
- Center for Cancer Biology, Vlaams Instituut voor Biotechnologie (VIB), Belgium; Department of Oncology, Katholieke Universiteit (KU) Leuven, Belgium; Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea.
| | - Seunghyeon Han
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea.
| | - Naveed Ilyas
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea; Department of Physics, Khalifa University of Science and Technology, Abu Dhabi, UAE.
| | - Yong-Moon Lee
- Department of Pathology, College of Medicine, Dankook University, South Korea.
| | - Boreom Lee
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju, South Korea.
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Lee YM, Lee B, Cho NH, Park JH. Beyond the Microscope: A Technological Overture for Cervical Cancer Detection. Diagnostics (Basel) 2023; 13:3079. [PMID: 37835821 PMCID: PMC10572593 DOI: 10.3390/diagnostics13193079] [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: 09/06/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023] Open
Abstract
Cervical cancer is a common and preventable disease that poses a significant threat to women's health and well-being. It is the fourth most prevalent cancer among women worldwide, with approximately 604,000 new cases and 342,000 deaths in 2020, according to the World Health Organization. Early detection and diagnosis of cervical cancer are crucial for reducing mortality and morbidity rates. The Papanicolaou smear test is a widely used screening method that involves the examination of cervical cells under a microscope to identify any abnormalities. However, this method is time-consuming, labor-intensive, subjective, and prone to human errors. Artificial intelligence techniques have emerged as a promising alternative to improve the accuracy and efficiency of Papanicolaou smear diagnosis. Artificial intelligence techniques can automatically analyze Papanicolaou smear images and classify them into normal or abnormal categories, as well as detect the severity and type of lesions. This paper provides a comprehensive review of the recent advances in artificial intelligence diagnostics of the Papanicolaou smear, focusing on the methods, datasets, performance metrics, and challenges. The paper also discusses the potential applications and future directions of artificial intelligence diagnostics of the Papanicolaou smear.
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Affiliation(s)
- Yong-Moon Lee
- Department of Pathology, College of Medicine, Dankook University, Cheonan 31116, Republic of Korea;
| | - Boreom Lee
- Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea;
| | - Nam-Hoon Cho
- Department of Pathology, Severance Hospital, College of Medicine, Yonsei University, Seoul 03722, Republic of Korea;
| | - Jae Hyun Park
- Department of Surgery, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University, Wonju 26492, Republic of Korea
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Alsalatie M, Alquran H, Mustafa WA, Zyout A, Alqudah AM, Kaifi R, Qudsieh S. A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images. Diagnostics (Basel) 2023; 13:2762. [PMID: 37685299 PMCID: PMC10487265 DOI: 10.3390/diagnostics13172762] [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: 07/20/2023] [Revised: 08/17/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
One of the most widespread health issues affecting women is cervical cancer. Early detection of cervical cancer through improved screening strategies will reduce cervical cancer-related morbidity and mortality rates worldwide. Using a Pap smear image is a novel method for detecting cervical cancer. Previous studies have focused on whole Pap smear images or extracted nuclei to detect cervical cancer. In this paper, we compared three scenarios of the entire cell, cytoplasm region, or nucleus region only into seven classes of cervical cancer. After applying image augmentation to solve imbalanced data problems, automated features are extracted using three pre-trained convolutional neural networks: AlexNet, DarkNet 19, and NasNet. There are twenty-one features as a result of these scenario combinations. The most important features are split into ten features by the principal component analysis, which reduces the dimensionality. This study employs feature weighting to create an efficient computer-aided cervical cancer diagnosis system. The optimization procedure uses the new evolutionary algorithms known as Ant lion optimization (ALO) and particle swarm optimization (PSO). Finally, two types of machine learning algorithms, support vector machine classifier, and random forest classifier, have been used in this paper to perform classification jobs. With a 99.5% accuracy rate for seven classes using the PSO algorithm, the SVM classifier outperformed the RF, which had a 98.9% accuracy rate in the same region. Our outcome is superior to other studies that used seven classes because of this focus on the tissues rather than just the nucleus. This method will aid physicians in diagnosing precancerous and early-stage cervical cancer by depending on the tissues, rather than on the nucleus. The result can be enhanced using a significant amount of data.
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Affiliation(s)
- Mohammed Alsalatie
- King Hussein Medical Center, Royal Jordanian Medical Service, The Institute of Biomedical Technology, Amman 11855, Jordan;
| | - Hiam Alquran
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan; (A.Z.); (A.M.A.)
| | - Wan Azani Mustafa
- Faculty of Electrical Engineering & Technology, Campus Pauh Putra, Universiti Malaysia Perlis, Arau 02600, Malaysia
- Advanced Computing (AdvCOMP), Centre of Excellence (CoE), Universiti Malaysia Perlis, Arau 02600, Malaysia
| | - Ala’a Zyout
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan; (A.Z.); (A.M.A.)
| | - Ali Mohammad Alqudah
- Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan; (A.Z.); (A.M.A.)
| | - Reham Kaifi
- College of Applied Medical Sciences, King Saud Bin Abdulaziz University for Health Sciences, Jeddah 21423, Saudi Arabia
- King Abdullah International Medical Research Center, Jeddah 22384, Saudi Arabia
| | - Suhair Qudsieh
- Department of Obstetrics and Gynecology, Faculty of Medicine, Yarmouk University, Irbid 21163, Jordan;
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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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Özbay E, Özbay FA. Interpretable pap-smear image retrieval for cervical cancer detection with rotation invariance mask generation deep hashing. Comput Biol Med 2023; 154:106574. [PMID: 36738706 DOI: 10.1016/j.compbiomed.2023.106574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Revised: 01/10/2023] [Accepted: 01/22/2023] [Indexed: 01/24/2023]
Abstract
Cervical cancer is a common disease in women, affecting their lives negatively and often resulting in death. Pap-smear tests are preferred by doctors as the primary tool in the early diagnosis and treatment of the disease. Physicians can be facilitated in the detection of five different categories of cervical cancer and similar cellular disease cases with the Pap-smear image retrieval technology. In this study, an algorithm for retrieval of cervical cancer images using hash coding with a Convolutional Neural Network (CNN) has been implemented. A sensitive deep hashing method combining interpretable mask generation and rotation invariance is proposed for cervical cancer detection. The distinctive features of cervical cancer cells with complex morphological features are focused on with the proposed hybrid dilated convolution spatial attention module and insignificant features are eliminated. Moreover, the loss function of Cauchy rotation invariance in terms of cervical cancer cell target is presented. In this way, the differences in the input samples are revealed, allowing the CNN to learn from different angles and achieve certain rotation invariance. The versatility and performance of the proposed method, as well as the efficiency of the loss function, have been tested on the SIPaKMeD and Mendeley LBC datasets consisting of cervical cancer images. In the experimental results obtained, it is shown that the proposed spatial attention module and rotational invariance deep hashing network generate high performance in cervical cancer image retrieval problems.
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Affiliation(s)
- Erdal Özbay
- Firat University, Faculty of Engineering, Computer Engineering, 23119, Elazig, Turkey.
| | - Feyza Altunbey Özbay
- Firat University, Faculty of Engineering, Software Engineering, 23119, Elazig, Turkey.
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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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Kupas D, Harangi B. Classification of Pap-smear cell images using deep convolutional neural network accelerated by hand-crafted features. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1452-1455. [PMID: 36083935 DOI: 10.1109/embc48229.2022.9871171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The classification of cells extracted from Pap-smears is in most cases done using neural network architectures. Nevertheless, the importance of features extracted with digital image processing is also discussed in many related articles. Decision support systems and automated analysis tools of Pap-smears often use these kinds of manually extracted, global features based on clinical expert opinion. In this paper, a solution is introduced where 29 different contextual features are combined with local features learned by a neural network so that it increases classification performance. The weight distribution between the features is also investigated leading to a conclusion that the numerical features are indeed forming an important part of the learning process. Furthermore, extensive testing of the presented methods is done using a dataset annotated by clinical experts. An increase of 3.2% in F1-Score value can be observed when using the combination of contextual and local features. Clinical Relevance - Analysis of images extracted from digital Pap-test using modern machine learning tools is discussed in many scientific papers. The manual classification of the cells can be time-consuming and expensive which requires a high amount of manual labor. Furthermore the result of the manual classification can also be uncertain due to interobserver variability. Considering these, any result that can lead to a more reliable highly accurate classification method is considered valuable in the field of cervical cancer screening.
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Yaman O, Tuncer T. Exemplar pyramid deep feature extraction based cervical cancer image classification model using pap-smear images. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103428] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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