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A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

Débora N. Diniz, Mariana T. Rezende, Andrea G. C. Bianchi, Claudia M. Carneiro, Eduardo J. S. Luz, Gladston J. P. Moreira, Daniela M. Ushizima, Fátima N. S. de Medeiros, Marcone J. F. Souza
2021 Journal of Imaging  
In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification.  ...  The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed.  ...  This work focused on the automatic classification of cervical cells obtained from Pap smear tests using deep learning.  ... 
doi:10.3390/jimaging7070111 fatcat:6ekefk2ekjbw7f35a2yuvjtwue

Hybrid AI-assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears

Xiaohui Zhu, Xiaoming Li, Kokhaur Ong, Wenli Zhang, Wencai Li, Longjie Li, David Young, Yongjian Su, Bin Shang, Linggan Peng, Wei Xiong, Yunke Liu (+22 others)
2021 Nature Communications  
We integrate XGBoost and a logical decision tree with these models to optimize the parameters given by the learning process, and we develop a complete cervical liquid-based cytology smear TBS diagnostic  ...  We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria.  ...  Code availability The source code for training the models mentioned in this work is available at https://gigantum.com/louguei/ncomms, or obtained by sending a request to the primary corresponding author  ... 
doi:10.1038/s41467-021-23913-3 pmid:34112790 fatcat:jfnl5jv72bg47ewhok7mwxqxk4

A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images

Wasswa William, Andrew Ware, Annabella Habinka Basaza-Ejiri, Johnes Obungoloch
2019 BioMedical Engineering OnLine  
Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable.  ...  This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images.  ...  The authors are also grateful to Mr Abraham Birungi from Pathology department of Mbarara University of Science and Technology, Uganda for providing support with pap-images.  ... 
doi:10.1186/s12938-019-0634-5 fatcat:frwymgtwgvcuxblkiwrqorx2fy

Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

Tamer AbuKhalil, Bassam A. Y. Alqaralleh, Ahmad H. Al-Omari
2022 Computers Materials & Continua  
Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist.  ...  This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images.  ...  ; (3) Machine Learning, pap-smear Images, Medical Imaging.  ... 
doi:10.32604/cmc.2022.024367 fatcat:la3fg6d73rbpdhrmz6cnggi6uq

Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

CSS Anupama, T. J. Benedict Jose, Heba F. Eid, Nojood O Aljehane, Fahd N. Al-Wesabi, Marwa Obayya, Anwer Mustafa Hilal
2022 Computers Materials & Continua  
Furthermore, a deep learning based Residual Network (ResNet50) method was executed as a feature extractor and CDF as a classifier to determine the class labels of the input pap smear images.  ...  In this aspect, this paper devises a new biomedical pap smear image classification using cascaded deep forest (BPSIC-CDF) model on Internet of Things (IoT) environment.  ...  In order to conquer the limitation related to the computer assisted Pap smear analyses system, manual analyses of Pap smear images using machine learning (ML) and image processing methods have been presented  ... 
doi:10.32604/cmc.2022.022701 fatcat:fih6vyxzcjfatguh5qeaw3rtfa

DeepPap: Deep Convolutional Networks for Cervical Cell Classification

Ling Zhang, Le Lu, Isabella Nogues, Ronald M. Summers, Shaoxiong Liu, Jianhua Yao
2017 IEEE journal of biomedical and health informatics  
In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets.  ...  Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal  ...  ACKNOWLEDGMENTS This work was supported in part by the Intramural Research Program at the NIH Clinical Center, and the National Natural Science Foundation of China (81501545).  ... 
doi:10.1109/jbhi.2017.2705583 pmid:28541229 fatcat:eox3nd3az5b53nhmrcmgiik3cy

Artificial Intelligence in Cervical Cancer Screening and Diagnosis

Xin Hou, Guangyang Shen, Liqiang Zhou, Yinuo Li, Tian Wang, Xiangyi Ma
2022 Frontiers in Oncology  
We, thus, aimed to discuss how AI can be used in cervical cancer screening and diagnosis, particularly to improve the accuracy of early diagnosis.  ...  Cervical cancer remains a leading cause of cancer death in women, seriously threatening their physical and mental health. It is an easily preventable cancer with early screening and diagnosis.  ...  ACKNOWLEDGMENTS We would like to thank our colleagues for their help in the completion of this manuscript.  ... 
doi:10.3389/fonc.2022.851367 pmid:35359358 pmcid:PMC8963491 fatcat:oilyysnpyvdl3a6dpzw3cpcmpi

Radial Basis Function Artificial Neural Network for the Investigation of Thyroid Cytological Lesions

Christos Fragopoulos, Abraham Pouliakis, Christos Meristoudis, Emmanouil Mastorakis, Niki Margari, Nicolaos Chroniaris, Nektarios Koufopoulos, Alexander G. Delides, Nicolaos Machairas, Vasileia Ntomi, Konstantinos Nastos, Ioannis G. Panayiotides (+3 others)
2020 Journal of Thyroid Research  
The major drawback in this approach is the automation of a procedure to accurately detect and measure cell nuclei from the digitized images.  ...  The proposed approach is promising to avoid misdiagnoses and assists the everyday practice of the cytopathology.  ...  Authors' Contributions Christos Fragopoulos, Abraham Pouliakis, and Christos Meristoudis equally contributed to this work.  ... 
doi:10.1155/2020/5464787 pmid:33299540 pmcid:PMC7707952 fatcat:yngeq4pdsjclnfl7xcda3lhhue

Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future

Abraham Pouliakis, Efrossyni Karakitsou, Niki Margari, Panagiotis Bountris, Maria Haritou, John panayiotides, Dimitrios Koutsouris, Petros Karakitsos
2016 Biomedical Engineering and Computational Biology  
Efforts to apply such systems within the laboratory test environment are required for their future uptake.  ...  STUDY DESIGN: A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed  ...  Despite the disadvantages, the literature research showed that ANNs have the capability of learning and may assist cytopathologists in their decision-making.  ... 
doi:10.4137/becb.s31601 pmid:26917984 pmcid:PMC4760671 fatcat:nm33kktghvbtdjtinwfx2d2id4

Artificial Intelligence: A Review of Progress and Prospects in Medicine and Healthcare

Saurav Mishra
2022 Journal of Electronics Electromedical Engineering and Medical Informatics  
The paper also discusses about the implementation opportunities various AI technologies like Machine Learning, Deep Learning, Reinforcement Learning, Natural Language Processing, Computer Vision provide  ...  diagnosis, assist physicians in making improved and precise clinical decisions.  ...  ., [96] apply ensemble based deep learning methods to localize the optical disc and automate the process of DR grading for an early diagnosis.  ... 
doi:10.35882/jeeemi.v4i1.1 fatcat:j2zcn22rl5f77nmy7rmbpr76ma

Comparison of Fine-Tuned Deep Convolutional Neural Networks for the Automated Classification of Lung Cancer Cytology Images with Integration of Additional Classifiers

Tetsuya Tsukamoto, Atsushi Teramoto, Ayumi Yamada, Yuka Kiriyama, Eiko Sakurai, Ayano Michiba, Kazuyoshi Imaizumi, Hiroshi Fujita
2022
The purpose of this study was to compare multiple deep convolutional neural network (DCNN) technique with subsequent additional classifiers in terms of accuracy and characteristics in each histology.  ...  For more precise classification, the figures of 3 histological probabilities were further applied to subsequent machine learning classifiers using Naïve Bayes (NB), Support vector machine (SVM), Random  ...  Funding Statement This research was partially supported by a Grant-in-Aid for Scientific Research on Innovative Areas (Multidisciplinary Computational Anatomy, No. 26108005), a Grant-in-Aid for Scientific  ... 
doi:10.31557/apjcp.2022.23.4.1315 pmid:35485691 fatcat:75ibxelpnzfbhovc7qq7raa2rq

Abstracts

2018 Virchows Archiv  
We then used a multinomial logistic regression test with a p-value threshold of 0.01 for more power.  ...  category in the 2016 WHO classification.  ...  Method: Participants to external quality assessment (EQA) schemes of the European Society in Pathology in 2016 and 2017 were invited to complete a survey.  ... 
doi:10.1007/s00428-018-2422-1 pmid:30120593 fatcat:rxb6dv66m5de5ocytkqgtk5cba

From the Editor-in-Chief

Kimon P. Valavanis
2018 Journal of Intelligent and Robotic Systems  
Acknowledgement We thank Mrs Christiane Gauvin for secretarial assistance, Acknowledgement The author is indebted to many colleagues and friends for the active discussions and exchanges both in the  ...  LTSI (A Bardou, JJ Bellanger, A Hernandez, F Wendling) and outside (P Bourgine, P Chauvel, J Demongeot, Y Meyer).  ...  Review of cell image segmentation methods can be found in papers [5] [6] [7] .  ... 
doi:10.1007/s10846-018-0847-z fatcat:zg6ye677mbgxvfxhlqw35n7ega