{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T19:02:41Z","timestamp":1789671761488,"version":"4.0.1"},"reference-count":113,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2022R1C1C1004590"],"award-info":[{"award-number":["2022R1C1C1004590"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009392","name":"Prince Sattam bin Abdulaziz University","doi-asserted-by":"publisher","award":["PSAU\/2024\/R\/1445"],"award-info":[{"award-number":["PSAU\/2024\/R\/1445"]}],"id":[{"id":"10.13039\/100009392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Gynaecological cancers encompass a spectrum of malignancies affecting the female reproductive system, comprising the cervix, uterus, ovaries, vulva, vagina, and fallopian tubes. The significant health threat posed by these cancers worldwide highlight the crucial need for techniques for early detection and prediction of gynaecological cancers. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published from 2013 up to 2023 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and encompass AI technique for the early detection and prediction of gynaecological cancers. Based on the study of different articles on gynaecological cancer, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score. This work highlights the impact of gynaecological cancer on women belonging to different age groups and regions of the world. A detailed categorization of the traditional techniques like physical-radiological, bio-physical and bio-chemical used to detect gynaecological cancer by health organizations is also presented in the study. Besides, this work also explores the methodology used by different researchers in which AI plays a crucial role in identifying cancer symptoms at earlier stages. The paper also investigates the pivotal study years, highlighting the periods when the highest number of research articles on gynaecological cancer are published. The challenges faced by researchers while performing AI-based research on gynaecological cancers are also highlighted in this work. The features and representations such as Magnetic Resonance Imaging (MRI), ultrasound, pap smear, pathological, etc., which proficient the AI algorithms in early detection of gynaecological cancer are also explored. This comprehensive review contributes to the understanding of the role of AI in improving the detection and prognosis of gynaecological cancers, and provides insights for future research directions and clinical applications. AI has the potential to substantially reduce mortality rates linked to gynaecological cancer in the future by enabling earlier identification, individualised risk assessment, and improved treatment techniques. This would ultimately improve patient outcomes and raise the standard of healthcare for all individuals.<\/jats:p>","DOI":"10.1007\/s10462-024-10872-6","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T02:01:32Z","timestamp":1722218492000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Comprehensive analysis of artificial intelligence techniques for gynaecological cancer: symptoms identification, prognosis and prediction"],"prefix":"10.1007","volume":"57","author":[{"given":"Sonam","family":"Gandotra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yogesh","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nandini","family":"Modi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaeyoung","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jana","family":"Shafi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Fazal","family":"Ijaz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"issue":"3","key":"10872_CR1","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1007\/s40995-022-01292-8","volume":"46","author":"C Ai","year":"2022","unstructured":"Ai C (2022) A method for cancer genomics feature selection based on LASSO-RFE. Iran J Sci Technol Trans A 46(3):731\u2013738","journal-title":"Iran J Sci Technol Trans A"},{"issue":"8","key":"10872_CR2","doi-asserted-by":"publisher","first-page":"4795","DOI":"10.21873\/anticanres.14482","volume":"40","author":"M Akazawa","year":"2020","unstructured":"Akazawa M, Hashimoto K (2020) Artificial intelligence in ovarian cancer diagnosis. Anticancer Res 40(8):4795\u20134800","journal-title":"Anticancer Res"},{"issue":"2","key":"10872_CR3","doi-asserted-by":"publisher","first-page":"170","DOI":"10.21449\/ijate.479404","volume":"6","author":"G Aksu","year":"2019","unstructured":"Aksu G, G\u00fczeller CO, Eser MT (2019) The effect of the normalization method used in different sample sizes on the success of artificial neural network model. Int J Assess Tools Educ 6(2):170\u2013192","journal-title":"Int J Assess Tools Educ"},{"issue":"3","key":"10872_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00551-6","volume":"2","author":"L Akter","year":"2021","unstructured":"Akter L, Ferdib-Al-Islam MM, Al-Rakhami MS, Haque MR (2021) Prediction of cervical cancer from behavior risk using machine learning techniques. SN Comput Sci 2(3):1\u201310","journal-title":"SN Comput Sci"},{"issue":"11","key":"10872_CR5","doi-asserted-by":"publisher","first-page":"4132","DOI":"10.3390\/s22114132","volume":"22","author":"N Al Mudawi","year":"2022","unstructured":"Al Mudawi N, Alazeb A (2022) A model for predicting cervical cancer using machine learning algorithms. Sensors (basel) 22(11):4132","journal-title":"Sensors (basel)"},{"key":"10872_CR6","doi-asserted-by":"crossref","unstructured":"Alam TM, Milhan M, Khan A, Iqbal MA, Wahab A, Mushtaq M (2019) Cervical cancer prediction through different screening methods using data mining. Int J Adv Comput Sci Appl 10(2)","DOI":"10.14569\/IJACSA.2019.0100251"},{"key":"10872_CR7","doi-asserted-by":"publisher","first-page":"104985","DOI":"10.1016\/j.compbiomed.2021.104985","volume":"139","author":"MM Ali","year":"2021","unstructured":"Ali MM et al (2021) Machine learning-based statistical analysis for early stage detection of cervical cancer. Comput Biol Med 139:104985","journal-title":"Comput Biol Med"},{"key":"10872_CR8","doi-asserted-by":"publisher","first-page":"305","DOI":"10.2147\/IJWH.S68979","volume":"7","author":"I Alkatout","year":"2015","unstructured":"Alkatout I et al (2015) Vulvar cancer: epidemiology, clinical presentation, and management options. Int J Womens Health 7:305\u2013313","journal-title":"Int J Womens Health"},{"issue":"4","key":"10872_CR10","first-page":"513","volume":"10","author":"F Asadi","year":"2020","unstructured":"Asadi F, Salehnasab C, Ajori L (2020) Supervised algorithms of machine learning for the prediction of cervical cancer. J Biomed Phys Eng 10(4):513\u2013522","journal-title":"J Biomed Phys Eng"},{"key":"10872_CR11","doi-asserted-by":"crossref","unstructured":"Avati A, Jung K, Harman S, Downing L, Ng A, Shah NH (2017) Improving palliative care with deep learning. In: International conference on bioinformatics and biomedicine (BIBM), pp 311\u2013316","DOI":"10.1109\/BIBM.2017.8217669"},{"issue":"1","key":"10872_CR12","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1186\/s12911-022-02087-y","volume":"22","author":"AS Azar","year":"2022","unstructured":"Azar AS et al (2022) Application of machine learning techniques for predicting survival in ovarian cancer. BMC Med Inform Decis Mak 22(1):345","journal-title":"BMC Med Inform Decis Mak"},{"key":"10872_CR13","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.ejmp.2021.04.016","volume":"83","author":"A Barrag\u00e1n-Montero","year":"2021","unstructured":"Barrag\u00e1n-Montero A et al (2021) Artificial intelligence and machine learning for medical imaging: a technology review. Phys Medica 83:242\u2013256","journal-title":"Phys Medica"},{"issue":"Suppl 2","key":"10872_CR14","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1002\/ijgo.12614","volume":"143","author":"JS Berek","year":"2018","unstructured":"Berek JS, Kehoe ST, Kumar L, Friedlander M (2018) Cancer of the ovary, fallopian tube, and peritoneum. Int J Gynaecol Obstet 143(Suppl 2):59\u201378","journal-title":"Int J Gynaecol Obstet"},{"issue":"6","key":"10872_CR15","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3322\/caac.21492","volume":"68","author":"F Bray","year":"2018","unstructured":"Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68(6):394\u2013424","journal-title":"CA Cancer J Clin"},{"issue":"4","key":"10872_CR16","first-page":"178","volume":"3","author":"AD Brett","year":"2015","unstructured":"Brett AD, Brown JK (2015) Quantitative computed tomography and opportunistic bone density screening by dual use of computed tomography scans. J Orthop Transl 3(4):178","journal-title":"J Orthop Transl"},{"key":"10872_CR17","unstructured":"Cervix uteri cancer. https:\/\/platform.who.int\/mortality\/themes\/theme-details\/topics\/indicator-groups\/indicator-group-details\/MDB\/cervix-uteri-cancer. Accessed 18 Jan 2024"},{"key":"10872_CR18","doi-asserted-by":"crossref","unstructured":"Ch N, Sai PP, Madhuri G, Reddy KS, Reddy DVB (2022) Artificial intelligence based cervical cancer risk prediction using M1 algorithms. In: 2022 international conference on emerging smart computing and informatics (ESCI 2022)","DOI":"10.1109\/ESCI53509.2022.9758241"},{"key":"10872_CR19","doi-asserted-by":"crossref","unstructured":"Chakradeo K, Vyawahare S, Pawar P (2019) Breast cancer recurrence prediction using machine learning. In: 2019 IEEE conference on information & communication technologies (CICT 2019)","DOI":"10.1109\/CICT48419.2019.9066248"},{"key":"10872_CR20","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5584004","author":"V Chandran","year":"2021","unstructured":"Chandran V et al (2021) Diagnosis of cervical cancer based on ensemble deep learning network using colposcopy images. Biomed Res Int. https:\/\/doi.org\/10.1155\/2021\/5584004","journal-title":"Biomed Res Int"},{"issue":"5","key":"10872_CR21","first-page":"46","volume":"13","author":"AK Chaudhuri","year":"2021","unstructured":"Chaudhuri AK, Ray A, Banerjee DK, Das A (2021) A multi-stage approach combining feature selection with machine learning techniques for higher prediction reliability and accuracy in cervical cancer diagnosis. Int J Intell Syst Appl 13(5):46\u201363","journal-title":"Int J Intell Syst Appl"},{"issue":"2","key":"10872_CR22","volume":"1","author":"T Chauhan","year":"2021","unstructured":"Chauhan T, Palivela H, Tiwari S (2021) Optimization and fine-tuning of DenseNet model for classification of COVID-19 cases in medical imaging. Int J Inf Manag Data Insights 1(2):100020","journal-title":"Int J Inf Manag Data Insights"},{"key":"10872_CR25","first-page":"632786","volume":"2009","author":"D Chen","year":"2009","unstructured":"Chen D, Xing K, Henson D, Sheng L, Schwartz AM, Cheng X (2009) Developing prognostic systems of cancer patients by ensemble clustering. J Biomed Biotechnol 2009:632786","journal-title":"J Biomed Biotechnol"},{"key":"10872_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.saa.2021.120355","volume":"265","author":"F Chen","year":"2022","unstructured":"Chen F et al (2022) Screening ovarian cancers with Raman spectroscopy of blood plasma coupled with machine learning data processing. Spectrochim Acta Part A 265:120355","journal-title":"Spectrochim Acta Part A"},{"key":"10872_CR24","doi-asserted-by":"publisher","DOI":"10.1177\/20552076231203802","volume-title":"Cervical cancer detection using K nearest neighbor imputer and stacked ensemble learningmodel","author":"X Chen","year":"2023","unstructured":"Chen X et al (2023) Cervical cancer detection using K nearest neighbor imputer and stacked ensemble learningmodel. DIGIT Health. https:\/\/doi.org\/10.1177\/20552076231203802"},{"issue":"1","key":"10872_CR26","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1002\/uog.23530","volume":"57","author":"F Christiansen","year":"2021","unstructured":"Christiansen F, Epstein EL, Smedberg E, \u00c5kerlund M, Smith K, Epstein E (2021) Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment. Ultrasound Obstet Gynecol 57(1):155\u2013163","journal-title":"Ultrasound Obstet Gynecol"},{"key":"10872_CR27","unstructured":"Corpus uteri cancer. https:\/\/platform.who.int\/mortality\/themes\/theme-details\/topics\/indicator-groups\/indicator-group-details\/MDB\/corpus-uteri-cancer. Accessed 18 Jan 2024"},{"issue":"4","key":"10872_CR28","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1016\/j.jmir.2019.09.005","volume":"50","author":"G Currie","year":"2019","unstructured":"Currie G, Hawk KE, Rohren E, Vial A, Klein R (2019) Machine learning and deep learning in medical imaging: intelligent imaging. J Med Imaging Radiat Sci 50(4):477\u2013487","journal-title":"J Med Imaging Radiat Sci"},{"key":"10872_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2022.109924","volume":"133","author":"LBV de Amorim","year":"2023","unstructured":"de Amorim LBV, Cavalcanti GDC, Cruz RMO (2023) The choice of scaling technique matters for classification performance. Appl Soft Comput 133:109924","journal-title":"Appl Soft Comput"},{"key":"10872_CR29","doi-asserted-by":"crossref","unstructured":"Deng X, Luo Y, Wang C (2019) Analysis of risk factors for cervical cancer based on machine learning methods. In: Proceedings of 2018 5th IEEE international conference on cloud computing and intelligence systems (CCIS 2018), pp 631\u2013635","DOI":"10.1109\/CCIS.2018.8691126"},{"issue":"4","key":"10872_CR30","doi-asserted-by":"publisher","first-page":"1419","DOI":"10.31557\/APJCP.2023.24.4.1419","volume":"24","author":"S Devi","year":"2023","unstructured":"Devi S, Gaikwad SR, Harikrishnan R (2023) Prediction and detection of cervical malignancy using machine learning models. Asian Pac J Cancer Prev 24(4):1419\u20131433","journal-title":"Asian Pac J Cancer Prev"},{"issue":"2","key":"10872_CR31","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1142\/S0219720005001004","volume":"3","author":"C Ding","year":"2011","unstructured":"Ding C, Peng H (2011) Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3(2):185\u2013205","journal-title":"J Bioinform Comput Biol"},{"issue":"16","key":"10872_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/ijerph17165993","volume":"17","author":"HC Dong","year":"2020","unstructured":"Dong HC, Dong HK, Yu MH, Lin YH, Chang CC (2020) Using deep learning with convolutional neural network approach to identify the invasion depth of endometrial cancer in myometrium using MR images: a pilot study. Int J Environ Res Public Health 17(16):1\u201318","journal-title":"Int J Environ Res Public Health"},{"key":"10872_CR33","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.comnet.2019.04.016","volume":"159","author":"M Elhoseny","year":"2019","unstructured":"Elhoseny M, Bian GB, Lakshmanaprabu SK, Shankar K, Singh AK, Wu W (2019) Effective features to classify ovarian cancer data in internet of medical things. Comput Netw 159:147\u2013156","journal-title":"Comput Netw"},{"issue":"1","key":"10872_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.xagr.2022.100154","volume":"3","author":"E Erdemoglu","year":"2023","unstructured":"Erdemoglu E et al (2023) Artificial intelligence for prediction of endometrial intraepithelial neoplasia and endometrial cancer risks in pre- and postmenopausal women. AJOG Glob Rep 3(1):100154","journal-title":"AJOG Glob Rep"},{"issue":"3","key":"10872_CR35","first-page":"526","volume":"14","author":"F Farzaneh","year":"2023","unstructured":"Farzaneh F et al (2023) Endometrial cancer in women with abnormal uterine bleeding: data mining classification methods. Casp J Int Med 14(3):526","journal-title":"Casp J Int Med"},{"issue":"7","key":"10872_CR36","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1080\/14737140.2020.1782750","volume":"20","author":"OT Filippova","year":"2020","unstructured":"Filippova OT, Leitao MM (2020) The current clinical approach to newly diagnosed uterine cancer. Expert Rev Anticancer Ther 20(7):581\u2013590","journal-title":"Expert Rev Anticancer Ther"},{"issue":"3","key":"10872_CR37","doi-asserted-by":"publisher","first-page":"e179","DOI":"10.1016\/S2589-7500(21)00278-8","volume":"4","author":"Y Gao","year":"2022","unstructured":"Gao Y et al (2022) Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study. Lancet Digit Heal 4(3):e179\u2013e187","journal-title":"Lancet Digit Heal"},{"key":"10872_CR38","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/j.future.2019.09.015","volume":"102","author":"A Ghoneim","year":"2020","unstructured":"Ghoneim A, Muhammad G, Hossain MS (2020) Cervical cancer classification using convolutional neural networks and extreme learning machines. Futur Gener Comput Syst 102:643\u2013649","journal-title":"Futur Gener Comput Syst"},{"issue":"23","key":"10872_CR39","doi-asserted-by":"publisher","first-page":"14814","DOI":"10.3390\/ijms232314814","volume":"23","author":"J Gonzalez-Bosquet","year":"2022","unstructured":"Gonzalez-Bosquet J et al (2022) Using genomic variation to distinguish ovarian high-grade serous carcinoma from benign fallopian tubes. Int J Mol Sci 23(23):14814","journal-title":"Int J Mol Sci"},{"issue":"1","key":"10872_CR40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s43586-021-00091-6","volume":"2","author":"M Greenacre","year":"2022","unstructured":"Greenacre M, Groenen PJF, Hastie T, Denza AI, Markos A, Tuzhilina E (2022) Principal component analysis. Nat Rev Methods Prim 2(1):1\u201321","journal-title":"Nat Rev Methods Prim"},{"key":"10872_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2012\/385175","volume":"2012","author":"V G\u00fcnther","year":"2012","unstructured":"G\u00fcnther V et al (2012) Malignant melanoma of the urethra: a rare histologic subdivision of vulvar cancer with a poor prognosis. Case Rep Obstet Gynecol 2012:1\u20136","journal-title":"Case Rep Obstet Gynecol"},{"key":"10872_CR41","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157\u20131182","journal-title":"J Mach Learn Res"},{"key":"10872_CR43","volume-title":"Mastering machine learning with scikit-learn","author":"G Hackeling","year":"2017","unstructured":"Hackeling G (2017) Mastering machine learning with scikit-learn, 2nd edn. Packt Publishing, Birmingham","edition":"2"},{"issue":"1","key":"10872_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-80068-9","volume":"11","author":"E Hodneland","year":"2021","unstructured":"Hodneland E et al (2021) Automated segmentation of endometrial cancer on MR images using deep learning. Sci Rep 11(1):1\u20138","journal-title":"Sci Rep"},{"issue":"9","key":"10872_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.xcrm.2021.100400","volume":"2","author":"R Hong","year":"2021","unstructured":"Hong R, Liu W, DeLair D, Razavian N, Feny\u00f6 D (2021) Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models. Cell Rep Med 2(9):100400","journal-title":"Cell Rep Med"},{"key":"10872_CR46","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.jare.2020.11.006","volume":"30","author":"YW Hsiao","year":"2021","unstructured":"Hsiao YW, Tao CL, Chuang EY, Lu TP (2021) A risk prediction model of gene signatures in ovarian cancer through bagging of GA-XGBoost models. J Adv Res 30:113\u2013122","journal-title":"J Adv Res"},{"key":"10872_CR48","unstructured":"Hsu C-W, Chang C-C, Lin C-J (2003) A practical guide to support vector classification. 1396\u20131400"},{"issue":"1","key":"10872_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40815-019-00764-1","volume":"22","author":"MJ Hsu","year":"2020","unstructured":"Hsu MJ, Chien YH, Wang WY, Hsu CC (2020) A convolutional fuzzy neural network architecture for object classification with small training database. Int J Fuzzy Syst 22(1):1\u201310","journal-title":"Int J Fuzzy Syst"},{"key":"10872_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.tice.2020.101347","volume":"65","author":"E Hussain","year":"2020","unstructured":"Hussain E, Mahanta LB, Das CR, Talukdar RK (2020) A comprehensive study on the multi-class cervical cancer diagnostic prediction on pap smear images using a fusion-based decision from ensemble deep convolutional neural network. Tissue Cell 65:101347","journal-title":"Tissue Cell"},{"issue":"10","key":"10872_CR50","doi-asserted-by":"publisher","first-page":"2809","DOI":"10.3390\/s20102809","volume":"20","author":"MF Ijaz","year":"2020","unstructured":"Ijaz MF, Attique M, Son Y (2020) Data-driven cervical cancer prediction model with outlier detection and over-sampling methods. Sensors 20(10):2809","journal-title":"Sensors"},{"issue":"10","key":"10872_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-021-04786-z","volume":"3","author":"S Jahan","year":"2021","unstructured":"Jahan S et al (2021) Automated invasive cervical cancer disease detection at early stage through suitable machine learning model. SN Appl Sci 3(10):1\u201317","journal-title":"SN Appl Sci"},{"key":"10872_CR52","first-page":"133","volume":"2021","author":"M Jha","year":"2021","unstructured":"Jha M, Gupta R, Saxena R (2021) Cervical cancer risk prediction using XGboost classifier. In: 2021 7th International Conference on Signal Processing and Communications (ICSC) 2021:133\u2013136","journal-title":"In: 2021 7th International Conference on Signal Processing and Communications (ICSC)"},{"key":"10872_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmedr.2023.102296","volume":"35","author":"B Jing","year":"2023","unstructured":"Jing B et al (2023) Development of prediction model to estimate future risk of ovarian lesions: a multi-center retrospective study. Prev Med Rep 35:102296","journal-title":"Prev Med Rep"},{"issue":"17","key":"10872_CR54","doi-asserted-by":"publisher","first-page":"4412","DOI":"10.3390\/cancers15174412","volume":"15","author":"H Karamti","year":"2023","unstructured":"Karamti H et al (2023) Improving prediction of cervical cancer using KNN imputed SMOTE features and multi-model ensemble learning approach. Cancers 15(17):4412","journal-title":"Cancers"},{"key":"10872_CR55","doi-asserted-by":"crossref","unstructured":"Karani H, Gangurde A, Dhumal G, Gautam W, Hiran S, Marathe A (2022) Comparison of performance of machine learning algorithms for cervical cancer classification. In: 2022 2nd advances in electrical, computing, communication and sustainable technologies (ICAECT 2022)","DOI":"10.1109\/ICAECT54875.2022.9807849"},{"issue":"19","key":"10872_CR56","doi-asserted-by":"publisher","first-page":"11947","DOI":"10.3390\/su141911947","volume":"14","author":"K Kaushik","year":"2022","unstructured":"Kaushik K et al (2022) A machine learning-based framework for the prediction of cervical cancer risk in women. Sustain 14(19):11947","journal-title":"Sustain"},{"issue":"20","key":"10872_CR57","doi-asserted-by":"publisher","first-page":"30399","DOI":"10.1007\/s11042-020-09607-w","volume":"80","author":"A Khamparia","year":"2021","unstructured":"Khamparia A, Gupta D, Rodrigues JJPC, de Albuquerque VHC (2021) DCAVN: cervical cancer prediction and classification using deep convolutional and variational autoencoder network. Multimed Tools Appl 80(20):30399\u201330415","journal-title":"Multimed Tools Appl"},{"issue":"1","key":"10872_CR58","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-07723-1","volume":"12","author":"M Kruczkowski","year":"2022","unstructured":"Kruczkowski M, Drabik-Kruczkowska A, Marciniak A, Tarczewska M, Kosowska M, Szczerska M (2022a) Predictions of cervical cancer identification by photonic method combined with machine learning. Sci Rep 12(1):1\u201311","journal-title":"Sci Rep"},{"issue":"1","key":"10872_CR59","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-07723-1","volume":"12","author":"M Kruczkowski","year":"2022","unstructured":"Kruczkowski M, Drabik-Kruczkowska A, Marciniak A, Tarczewska M, Kosowska M, Szczerska M (2022b) Predictions of cervical cancer identification by photonic method combined with machine learning. Sci Rep 12(1):1\u201311","journal-title":"Sci Rep"},{"issue":"1","key":"10872_CR60","doi-asserted-by":"publisher","first-page":"231","DOI":"10.5455\/medscience.2022.09.207","volume":"12","author":"Z Kucukakcali","year":"2023","unstructured":"Kucukakcali Z, Ozhan O, Kucukakcali Z, Cicek IB (2023) Machine learning-based ovarian cancer prediction with XGboost and stochastic gradient boosting models. Med Sci 12(1):231\u2013238","journal-title":"Med Sci"},{"issue":"1","key":"10872_CR61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13048-020-00700-0","volume":"13","author":"A Laios","year":"2020","unstructured":"Laios A, Gryparis A, Dejong D, Hutson R, Theophilou G, Leach C (2020) Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models. J Ovarian Res 13(1):1\u20138","journal-title":"J Ovarian Res"},{"key":"10872_CR62","unstructured":"Lee K, Kim S, Lim S, Choi S, Oh S (2019) Tsallis reinforcement learning: a unified framework for maximum entropy reinforcement learning. arXiv:1902.00137"},{"issue":"4","key":"10872_CR63","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/S0301-5629(96)00196-2","volume":"23","author":"S Levi","year":"1997","unstructured":"Levi S (1997) The history of ultrasound in gynecology 1950\u20131980. Ultrasound Med Biol 23(4):481\u2013552","journal-title":"Ultrasound Med Biol"},{"key":"10872_CR64","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2020.104195","volume":"141","author":"M Lu","year":"2020","unstructured":"Lu M et al (2020a) Using machine learning to predict ovarian cancer. Int J Med Inform 141:104195","journal-title":"Int J Med Inform"},{"key":"10872_CR65","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.future.2019.12.033","volume":"106","author":"J Lu","year":"2020","unstructured":"Lu J, Song E, Ghoneim A, Alrashoud M (2020b) Machine learning for assisting cervical cancer diagnosis: an ensemble approach. Futur Gener Comput Syst 106:199\u2013205","journal-title":"Futur Gener Comput Syst"},{"issue":"7","key":"10872_CR66","first-page":"204","volume":"14","author":"TR Mahesh","year":"2022","unstructured":"Mahesh TR, Kaladevi AC, Balajee JM, Vivek V, Prabu M, Muthukumaran V (2022) An efficient ensemble method using K-fold cross validation for the early detection of benign and malignant breast cancer. Int J Integr Eng 14(7):204\u2013216","journal-title":"Int. J. Integr. Eng."},{"issue":"3","key":"10872_CR67","doi-asserted-by":"publisher","first-page":"112","DOI":"10.4103\/2278-330X.187575","volume":"5","author":"A Maheshwari","year":"2016","unstructured":"Maheshwari A, Kumar N, Mahantshetty U (2016) Gynecological cancers: a summary of published Indian data. South Asian J Cancer 5(3):112\u2013120","journal-title":"South Asian J Cancer"},{"key":"10872_CR68","first-page":"1","volume":"9","author":"M Mehmood","year":"2021","unstructured":"Mehmood M, Rizwan M, Gregusml M, Abbas S (2021) Machine learning assisted cervical cancer detection. Front Public Heal 9:1\u201314","journal-title":"Front Public Heal"},{"issue":"1","key":"10872_CR69","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-95128-x","volume":"11","author":"M Mohammed","year":"2021","unstructured":"Mohammed M, Mwambi H, Mboya IB, Elbashir MK, Omolo B (2021) A stacking ensemble deep learning approach to cancer type classification based on TCGA data. Sci Rep 11(1):1\u201322","journal-title":"Sci Rep"},{"issue":"10","key":"10872_CR70","doi-asserted-by":"publisher","first-page":"1006","DOI":"10.1016\/j.jclinepi.2009.06.005","volume":"62","author":"D Moher","year":"2009","unstructured":"Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol 62(10):1006\u20131012","journal-title":"J Clin Epidemiol"},{"issue":"3","key":"10872_CR71","doi-asserted-by":"publisher","first-page":"284","DOI":"10.30699\/ijp.2021.135323.2482","volume":"16","author":"NM Namini","year":"2021","unstructured":"Namini NM, Abdollahi A, Movahedi M, Razavi AE, Saghiri R (2021) HE4, a new potential tumor marker for early diagnosis and predicting of breast cancer progression. Iran J Pathol 16(3):284","journal-title":"Iran J Pathol"},{"issue":"6","key":"10872_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-019-0645-7","volume":"1","author":"B Nithya","year":"2019","unstructured":"Nithya B, Ilango V (2019) Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction. SN Appl Sci 1(6):1\u201316","journal-title":"SN Appl Sci"},{"key":"10872_CR73","doi-asserted-by":"publisher","DOI":"10.5772\/58947","author":"TK Nyengidiki","year":"2015","unstructured":"Nyengidiki TK, Nyengidiki TK (2015) Screening methods for gynaecological cancers. Contemp Gynecol Pract. https:\/\/doi.org\/10.5772\/58947","journal-title":"Contemp Gynecol Pract"},{"issue":"3","key":"10872_CR74","doi-asserted-by":"publisher","first-page":"887","DOI":"10.3390\/agronomy13030887","volume":"13","author":"MO Ojo","year":"2023","unstructured":"Ojo MO, Zahid A (2023) Improving deep learning classifiers performance via preprocessing and class imbalance approaches in a plant disease detection pipeline. Agron 13(3):887","journal-title":"Agron"},{"key":"10872_CR75","unstructured":"Ovary cancer. https:\/\/platform.who.int\/mortality\/themes\/theme-details\/topics\/indicator-groups\/indicator-group-details\/MDB\/ovary-cancer. Accessed 18 Jan 2024"},{"key":"10872_CR76","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.jclinepi.2021.02.003","volume":"134","author":"MJ Page","year":"2021","unstructured":"Page MJ et al (2021) Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement. J Clin Epidemiol 134:103\u2013112","journal-title":"J Clin Epidemiol"},{"issue":"6","key":"10872_CR77","first-page":"561","volume":"91","author":"SM Penny","year":"2020","unstructured":"Penny SM (2020) Ovarian cancer: an overview. Radiol Technol 91(6):561\u2013575","journal-title":"Radiol Technol"},{"issue":"1","key":"10872_CR78","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41747-018-0061-6","volume":"2","author":"F Pesapane","year":"2018","unstructured":"Pesapane F, Codari M, Sardanelli F (2018) Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2(1):1\u201310","journal-title":"Eur Radiol Exp"},{"issue":"4","key":"10872_CR79","doi-asserted-by":"publisher","first-page":"601","DOI":"10.1097\/RCT.0b013e31818d4279","volume":"33","author":"S Peungjesada","year":"2009","unstructured":"Peungjesada S, Bhosale PR, Balachandran A, Iyer RB (2009) Magnetic resonance imaging of endometrial carcinoma. J Comput Assist Tomogr 33(4):601\u2013608","journal-title":"J Comput Assist Tomogr"},{"issue":"3","key":"10872_CR80","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1016\/j.ygyno.2020.09.047","volume":"159","author":"AM Praiss","year":"2020","unstructured":"Praiss AM et al (2020) Using machine learning to create prognostic systems for endometrial cancer. Gynecol Oncol 159(3):744\u2013750","journal-title":"Gynecol Oncol"},{"issue":"1","key":"10872_CR81","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.2991\/ijcis.d.200730.003","volume":"13","author":"S Priya","year":"2020","unstructured":"Priya S, Karthikeyan NK (2020) A heuristic and ANN based classification model for early screening of cervical cancer. Int J Comput Intell Syst 13(1):1092\u20131100","journal-title":"Int J Comput Intell Syst"},{"key":"10872_CR82","first-page":"1","volume":"20","author":"P Probst","year":"2018","unstructured":"Probst P, Boulesteix AL, Bischl B (2018) Tunability: importance of hyperparameters of machine learning algorithms. J Mach Learn Res 20:1\u201332","journal-title":"J Mach Learn Res"},{"key":"10872_CR83","doi-asserted-by":"publisher","DOI":"10.3389\/fnano.2022.972421","volume":"4","author":"S Prusty","year":"2022","unstructured":"Prusty S, Patnaik S, Dash SK (2022) SKCV: stratified K-fold cross-validation on ML classifiers for predicting cervical cancer. Front Nanotechnol 4:972421","journal-title":"Front Nanotechnol"},{"issue":"7","key":"10872_CR84","doi-asserted-by":"publisher","first-page":"1719","DOI":"10.1002\/ijc.31961","volume":"145","author":"O Raglan","year":"2019","unstructured":"Raglan O et al (2019) Risk factors for endometrial cancer: an umbrella review of the literature. Int J Cancer 145(7):1719\u20131730","journal-title":"Int J Cancer"},{"key":"10872_CR85","doi-asserted-by":"publisher","first-page":"100797","DOI":"10.1016\/j.measen.2023.100797","volume":"27","author":"TN Ravishankar","year":"2023","unstructured":"Ravishankar TN, Jadhav HM, Kumar NS, Ambala S, Pillai M (2023) A deep learning approach for ovarian cysts detection and classification (OCD-FCNN) using fuzzy convolutional neural network. Meas Sensors 27:100797","journal-title":"Meas Sensors"},{"issue":"1\u20132","key":"10872_CR86","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1023\/A:1025667309714","volume":"53","author":"M Robnik-\u0160ikonja","year":"2003","unstructured":"Robnik-\u0160ikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53(1\u20132):23\u201369","journal-title":"Mach Learn"},{"issue":"17","key":"10872_CR87","doi-asserted-by":"publisher","first-page":"S23","DOI":"10.12968\/bjon.2013.22.Sup17.S23","volume":"22","author":"C Rooth","year":"2013","unstructured":"Rooth C (2013) Ovarian cancer: risk factors, treatment and management. Br J Nurs 22(17):S23\u2013S30","journal-title":"Br J Nurs"},{"key":"10872_CR88","first-page":"451","volume":"2021","author":"QS Setiawan","year":"2021","unstructured":"Setiawan QS, Rustam Z, Pandelaki J (2021) Comparison of Naive Bayes and support vector machine with grey wolf optimization feature selection for cervical cancer data classification. In: 2021 International Conference on Decision Aid Sciences and Application (DASA) 2021:451\u2013455","journal-title":"In: 2021 International Conference on Decision Aid Sciences and Application (DASA)"},{"issue":"5","key":"10872_CR89","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1007\/s12553-019-00375-8","volume":"9","author":"M Sharma","year":"2019","unstructured":"Sharma M (2019) Cervical cancer prognosis using genetic algorithm and adaptive boosting approach. Health Technol (berl) 9(5):877\u2013886","journal-title":"Health Technol (berl)"},{"issue":"3","key":"10872_CR90","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1016\/j.ygyno.2022.07.024","volume":"166","author":"P Shrestha","year":"2022","unstructured":"Shrestha P et al (2022) A systematic review on the use of artificial intelligence in gynecologic imaging\u2014background, state of the art, and future directions. Gynecol Oncol 166(3):596\u2013605","journal-title":"Gynecol Oncol"},{"issue":"5","key":"10872_CR91","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1016\/j.acra.2020.02.028","volume":"28","author":"A Stanzione","year":"2021","unstructured":"Stanzione A et al (2021) Deep myometrial infiltration of endometrial cancer on MRI: a radiomics-powered machine learning pilot study. Acad Radiol 28(5):737\u2013744","journal-title":"Acad Radiol"},{"issue":"12","key":"10872_CR92","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0226765","volume":"14","author":"GF Stark","year":"2019","unstructured":"Stark GF, Hart GR, Nartowt BJ, Deng J (2019) Predicting breast cancer risk using personal health data and machine learning models. PLoS ONE 14(12):e0226765","journal-title":"PLoS ONE"},{"issue":"4","key":"10872_CR93","first-page":"446","volume":"13","author":"G Sun","year":"2017","unstructured":"Sun G, Li S, Cao Y, Lang F (2017) Cervical cancer diagnosis based on random forest. Int J Perform Eng 13(4):446","journal-title":"Int J Perform Eng"},{"key":"10872_CR94","first-page":"4278","volume":"2017","author":"C Szegedy","year":"2016","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2016) Inception-v4, inception-ResNet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence 2017:4278\u20134284","journal-title":"In: Thirty-First AAAI Conference on Artificial Intelligence"},{"issue":"3","key":"10872_CR95","first-page":"205","volume":"53","author":"A Tak","year":"2022","unstructured":"Tak A, Parihar PM, Fatehpuriya DS, Singh Y (2022) Optimised feature selection and cervical cancer prediction using Machine learning classification. Scr Med (brno) 53(3):205\u2013211","journal-title":"Scr Med (brno)"},{"issue":"1","key":"10872_CR96","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12935-020-01742-6","volume":"21","author":"X Tan","year":"2021","unstructured":"Tan X et al (2021) Automatic model for cervical cancer screening based on convolutional neural network: a retrospective, multicohort, multicenter study. Cancer Cell Int 21(1):1\u201310","journal-title":"Cancer Cell Int"},{"key":"10872_CR97","doi-asserted-by":"publisher","first-page":"04004","DOI":"10.1051\/shsconf\/202110204004","volume":"102","author":"JJ Tanimu","year":"2021","unstructured":"Tanimu JJ, Hamada M, Hassan M, Ilu SY (2021) A contemporary machine learning method for accurate prediction of cervical cancer. SHS Web Conf 102:04004","journal-title":"SHS Web Conf"},{"key":"10872_CR98","doi-asserted-by":"publisher","first-page":"463","DOI":"10.3390\/electronics11030463","volume":"11","author":"JJ Tanimu","year":"2022","unstructured":"Tanimu JJ, Hamada M, Hassan M, Kakudi H, Abiodun J (2022) A machine learning method for classification of cervical cancer. Electronics 11:463","journal-title":"Electronics"},{"issue":"1","key":"10872_CR99","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3390\/cancers13010037","volume":"13","author":"AH Taylor","year":"2020","unstructured":"Taylor AH, Tortolani D, Ayakannu T, Konje JC, Maccarrone M (2020) (Endo)cannabinoids and gynaecological cancers. Cancers 13(1):37","journal-title":"Cancers"},{"issue":"6","key":"10872_CR100","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1007\/s00521-013-1359-1","volume":"24","author":"CJ Tseng","year":"2014","unstructured":"Tseng CJ, Lu CJ, Chang CC, Den Chen G (2014) Application of machine learning to predict the recurrence-proneness for cervical cancer. Neural Comput Appl 24(6):1311\u20131316","journal-title":"Neural Comput Appl"},{"key":"10872_CR101","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.artmed.2017.06.003","volume":"78","author":"CJ Tseng","year":"2017","unstructured":"Tseng CJ, Lu CJ, Chang CC, Den Chen G, Cheewakriangkrai C (2017) Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence. Artif Intell Med 78:47\u201354","journal-title":"Artif Intell Med"},{"issue":"1","key":"10872_CR102","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-022-00808-3","volume":"22","author":"A Urushibara","year":"2022","unstructured":"Urushibara A et al (2022) The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists. BMC Med Imaging 22(1):1\u201314","journal-title":"BMC Med Imaging"},{"key":"10872_CR103","doi-asserted-by":"crossref","unstructured":"Vikas, Kaur P (2021) Lung cancer detection using chi-square feature selection and support vector machine algorithm. Int J Adv Trends Comput Sci Eng 10(3):2050\u20132061","DOI":"10.30534\/ijatcse\/2021\/801032021"},{"issue":"2","key":"10872_CR104","first-page":"3297","volume":"70","author":"MI Waly","year":"2022","unstructured":"Waly MI, Sikkandar MY, Aboamer MA, Kadry S, Thinnukool O (2022) Optimal deep convolution neural network for cervical cancer diagnosis model. Comput Mater Contin 70(2):3297\u20133309","journal-title":"Comput Mater Contin"},{"key":"10872_CR105","unstructured":"Weston J, Mukherjee S, Chapelle O, Pontil M, Poggio T, Vapnik V (2000) Feature selection for SVMs. Adv Neural Inf Process Syst 13"},{"issue":"1","key":"10872_CR106","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1821\/1\/012007","volume":"1821","author":"VVP Wibowo","year":"2021","unstructured":"Wibowo VVP, Rustam Z, Hartini S, Maulidina F, Wirasati I, Sadewo W (2021) Ovarian cancer classification using K-nearest neighbor and support vector machine. J Phys Conf Ser 1821(1):012007","journal-title":"J Phys Conf Ser"},{"issue":"1\u20133","key":"10872_CR107","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","volume":"2","author":"S Wold","year":"1987","unstructured":"Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1\u20133):37\u201352","journal-title":"Chemom Intell Lab Syst"},{"key":"10872_CR109","doi-asserted-by":"publisher","first-page":"25189","DOI":"10.1109\/ACCESS.2017.2763984","volume":"5","author":"W Wu","year":"2017","unstructured":"Wu W, Zhou H (2017) Data-driven diagnosis of cervical cancer with support vector machine-based approaches. IEEE Access 5:25189\u201325195","journal-title":"IEEE Access"},{"issue":"3","key":"10872_CR108","doi-asserted-by":"publisher","first-page":"20180289","DOI":"10.1042\/BSR20180289","volume":"38","author":"M Wu","year":"2018","unstructured":"Wu M, Yan C, Liu H, Liu Q (2018) Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks. Biosci Rep 38(3):20180289","journal-title":"Biosci Rep"},{"issue":"1","key":"10872_CR110","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1038\/s41746-022-00559-z","volume":"5","author":"P Xue","year":"2022","unstructured":"Xue P et al (2022) Deep learning in image-based breast and cervical cancer detection: a systematic review and meta-analysis. NPJ Digit Med 5(1):19","journal-title":"NPJ Digit Med"},{"issue":"1","key":"10872_CR111","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1080\/03610918.2020.1850790","volume":"52","author":"Y Zhong","year":"2023","unstructured":"Zhong Y, Chalise P, He J (2023) Nested cross-validation with ensemble feature selection and classification model for high-dimensional biological data. Commun Stat 52(1):110\u2013125","journal-title":"Commun Stat"},{"key":"10872_CR112","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104487","volume":"134","author":"X Zhu","year":"2021","unstructured":"Zhu X, Ying J, Yang H, Fu L, Li B, Jiang B (2021) Detection of deep myometrial invasion in endometrial cancer MR imaging based on multi-feature fusion and probabilistic support vector machine ensemble. Comput Biol Med 134:104487","journal-title":"Comput Biol Med"},{"issue":"10","key":"10872_CR113","doi-asserted-by":"publisher","first-page":"1703","DOI":"10.3390\/diagnostics13101703","volume":"13","author":"B Ziyambe","year":"2023","unstructured":"Ziyambe B et al (2023) A deep learning framework for the prediction and diagnosis of ovarian cancer in pre- and post-menopausal women. Diagnostics 13(10):1703","journal-title":"Diagnostics"}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10872-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-024-10872-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-024-10872-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T05:23:26Z","timestamp":1723872206000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-024-10872-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,29]]},"references-count":113,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["10872"],"URL":"https:\/\/doi.org\/10.1007\/s10462-024-10872-6","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,29]]},"assertion":[{"value":"29 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"220"}}