تشخیص سلول های پیش سرطانی دهانه رحم با استفاده ا ز طبقه بندی ترکیبی برروی تصاوی ر پاپ اسمیر.
Background. Cervical cancer begins in superficial cells and over time can invade deeper tissues and surrounding tissues. This paper presents a creative idea of using an ensemble classification algorithm that improves the predictive performance of an artificial intelligence system based on cervical c...
| Publicado en: | Medical Journal of Tabriz University of Medical Sciences Vol. 44; no. 4; pp. 281 - 290 |
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| Autores principales: | , |
| Formato: | pictorial research tables/charts Journal Article |
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Tabriz University of Medical Sciences
Oct2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=160339127&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 160339127 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 27832031 N3KB jtl: Medical Journal of Tabriz University of Medical Sciences issn: 27832031 maglogo: N pubinfo: dt: Oct2022 vid: 44 iid: 4 pid: 69573 pub: Tabriz University of Medical Sciences artinfo: ui: 160339127 160339127 160339127 10.34172/mj.2022.034 160339127 ppf: 281 ppct: 9 formats: fmt: @attributes: type: P tig: atl: تشخیص سلول های پیش سرطانی دهانه رحم با استفاده ا ز طبقه بندی ترکیبی برروی تصاوی ر پاپ اسمیر. aug: au: مرضیه لطفی محمدرضا مومن زاد affil: گروه بیوالکتریک، دانشکده فنی و مهندسی، موسسه آموزش عالی علوم و فناوری سپاهان، اصفهان، ایران. sug: subj: Cervical Smears Utilization Precancerous Conditions Cervix Neoplasms Diagnosis Artificial Intelligence Algorithms Classification Human Early Detection of Cancer Cervix Neoplasms Risk Factors Machine Learning Carcinoma Computers and Computerization Risk Assessment Descriptive Statistics ab: Background. Cervical cancer begins in superficial cells and over time can invade deeper tissues and surrounding tissues. This paper presents a creative idea of using an ensemble classification algorithm that improves the predictive performance of an artificial intelligence system based on cervical cancer screening. This study aimed to classify Pap-smear images by different machine learning methods to achieve high accuracy detection. Methods. This study was performed on 917 Pap-smear images from the Herlev public database. In the feature extraction stage, 20 geometric features and 76 texture features were extracted. After that, using ensemble classification method, the images were classified into two categories (i.e., normal and abnormal) and then into seven categories (i.e., superficial epithelial, intermediate epithelial, columnar epithelial, mild dysplasia, moderate dysplasia, severe dysplasia and carcinoma) and the accuracy of the proposed method was evaluated. Results. The algorithm in the ensemble classification was able to achieve accuracy of 99.9% with a processing time of 0.028 second in the two-class classification and accuracy of 76.5% with a processing time of 0.033 second in the seven-class classification. Conclusion. Based on the results, the designed algorithm can be used as a computer aided diagnostic tool to increase the accuracy and speed of predicting the risk of cervical cancer. Practical Implications. Cervical cancer is one of the most common cancers among women. Early diagnosis of the disease can save various costs and prevent the patients’ frequent visits to medical centers. This research proposed an artificial intelligence method for automatic classification of cervical cells and improving the accuracy of diagnosis. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: Persian refInfo: holdings: @attributes: islocal: N |
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