{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T21:02:08Z","timestamp":1782421328382,"version":"3.54.5"},"reference-count":30,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Competitive Basic Research Grant","award":["142\/E5\/PG.02.00.PT\/2022"],"award-info":[{"award-number":["142\/E5\/PG.02.00.PT\/2022"]}]},{"name":"Faculty of Medicine, Universitas Sriwijaya, Indonesia","award":["142\/E5\/PG.02.00.PT\/2022"],"award-info":[{"award-number":["142\/E5\/PG.02.00.PT\/2022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Precancerous screening using visual inspection with acetic acid (VIA) is suggested by the World Health Organization (WHO) for low\u2013middle-income countries (LMICs). However, because of the limited number of gynecological oncologist clinicians in LMICs, VIA screening is primarily performed by general clinicians, nurses, or midwives (called medical workers). However, not being able to recognize the significant pathophysiology of human papilloma virus (HPV) infection in terms of the columnar epithelial-cell, squamous epithelial-cell, and white-spot regions with abnormal blood vessels may be further aggravated by VIA screening, which achieves a wide range of sensitivity (49\u201398%) and specificity (75\u201391%); this might lead to a false result and high interobserver variances. Hence, the automated detection of the columnar area (CA), subepithelial region of the squamocolumnar junction (SCJ), and acetowhite (AW) lesions is needed to support an accurate diagnosis. This study proposes a mask-RCNN architecture to simultaneously segment, classify, and detect CA and AW lesions. We conducted several experiments using 262 images of VIA+ cervicograms, and 222 images of VIA\u2212cervicograms. The proposed model provided a satisfactory intersection over union performance for the CA of about 63.60%, and AW lesions of about 73.98%. The dice similarity coefficient performance was about 75.67% for the CA and about 80.49% for the AW lesion. It also performed well in cervical-cancer precursor-lesion detection, with a mean average precision of about 86.90% for the CA and of about 100% for the AW lesion, while also achieving 100% sensitivity and 92% specificity. Our proposed model with the instance segmentation approach can segment, detect, and classify cervical-cancer precursor lesions with satisfying performance only from a VIA cervicogram.<\/jats:p>","DOI":"10.3390\/s22155489","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T04:52:47Z","timestamp":1658724767000},"page":"5489","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Automated Precancerous Lesion Screening Using an Instance Segmentation Technique for Improving Accuracy"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6438-3050","authenticated-orcid":false,"given":"Patiyus","family":"Agustiansyah","sequence":"first","affiliation":[{"name":"Doctoral Program, Biology Science, Faculty of Medicine, Universitas Sriwijaya, Palembang 30139, Indonesia"},{"name":"Division of Oncology-Gynecology, Department of Obstetrics and Gynecology, Mohammad Hoesin General Hospital, Palembang 30126, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8024-2952","authenticated-orcid":false,"given":"Siti","family":"Nurmaini","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laila","family":"Nuranna","sequence":"additional","affiliation":[{"name":"Obstetrics & Gynecology Department, Faculty of Medicine, University of Indonesia, Jakarta 10430, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2217-367X","authenticated-orcid":false,"given":"Irfannuddin","family":"Irfannuddin","sequence":"additional","affiliation":[{"name":"Obstetrics & Gynecology Department, Faculty of Medicine, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rizal","family":"Sanif","sequence":"additional","affiliation":[{"name":"Obstetrics & Gynecology Department, Faculty of Medicine, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Legiran","family":"Legiran","sequence":"additional","affiliation":[{"name":"Obstetrics & Gynecology Department, Faculty of Medicine, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Naufal","family":"Rachmatullah","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gavira Olipa","family":"Florina","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ade Iriani","family":"Sapitri","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0229-5717","authenticated-orcid":false,"given":"Annisa","family":"Darmawahyuni","sequence":"additional","affiliation":[{"name":"Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: Globocan. Estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","unstructured":"International Agency for Research on Cancer (WHO) (2022, February 21). Indonesia\u2014Global Cancer Observatory 2018, Available online: https:\/\/gco.iarc.fr\/today\/data\/factsheets\/populations\/360-indonesia-fact-sheets.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Herfs, M., Soong, T.R., Delvenne, P., and Crum, C.P. (2017). Deciphering the multifactorial susceptibility of mucosal junction cells to HPV Infection and related carcinogenesis. Viruses, 9.","DOI":"10.3390\/v9040085"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1002\/path.4110","article-title":"A novel blueprint for \u2018top down\u2019 differentiation defines the cervical squamocolumnar junction during development, reproductive life, and neoplasia","volume":"229","author":"Herfs","year":"2013","journal-title":"J. Pathol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1002\/path.4533","article-title":"Carcinogenic HPV infection in the cervical squamo-columnar junction","volume":"236","author":"Mirkovic","year":"2015","journal-title":"J. Pathol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/nrdp.2016.86","article-title":"Carcinogenic human papillomavirus infection","volume":"2","author":"Schiffman","year":"2016","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1080\/14737159.2019.1648213","article-title":"Diagnostics advances in technologies for cervical cancer detection in low-resource settings","volume":"19","author":"Kundrod","year":"2019","journal-title":"Expert Rev. Mol. Diagn."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1097\/CEJ.0b013e328364f273","article-title":"Review of the current knowledge on the epidemiology, pathogenesis, and prevention of human papillomavirus infection","volume":"23","author":"Asiaf","year":"2007","journal-title":"Eur. J. Cancer Prev."},{"key":"ref_9","first-page":"680","article-title":"Human papilloma virus infection and cervical cancer: Pathogenesis and epidemiology","volume":"1","author":"Santos","year":"2007","journal-title":"Commun. Curr. Res. Educ. Top. Trends Appl. Microbiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"242","DOI":"10.9745\/GHSP-D-18-00206","article-title":"Is it time to move beyond visual inspection with acetic acid for cervical cancer screening? What is the role of persistent HPV","volume":"6","author":"Silkensen","year":"2018","journal-title":"Glob. Health Sci. Pr."},{"key":"ref_11","unstructured":"World Health Organization (2022, February 21). WHO Guidelines for Screening and Treatment of Precancerous Lesions for Cervical Cancer Prevention, Available online: https:\/\/apps.who.int\/iris\/bitstream\/handle\/10665\/96735\/WHO_RHR_13.21_eng.pdf?sequence=1."},{"key":"ref_12","unstructured":"WHO (2020, October 29). WHO Global World Health Assembly Adopts Global Strategy to Accelerate Cervical Cancer Elimination, Available online: https:\/\/www.who.int\/news\/item\/19-08-2020-world-health-assembly-adopts-global-strategy-to-accelerate-cervical-cancer-elimination."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.ijgo.2015.07.024","article-title":"Systematic reviews and meta-analyses of the accuracy of HPV tests, visual inspection with acetic acid, cytology, and colposcopy","volume":"132","author":"Mustafa","year":"2015","journal-title":"Int. J. Gynecol. Obstet."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"100","DOI":"10.3322\/caac.21392","article-title":"Cancer screening in the United States, 2017: A review of current American Cancer Society guidelines and current issues in cancer screening","volume":"67","author":"Smith","year":"2017","journal-title":"CA Cancer J. Clin."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"39","DOI":"10.19127\/mbsjohs.521193","article-title":"The challenge of pathological diagnosis for precancerous cervical lesions","volume":"5","year":"2019","journal-title":"Middle Black Sea J. Health Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.ijgo.2010.10.012","article-title":"Accuracy of visual inspection with acetic acid for cervical cancer screening","volume":"113","author":"Sauvaget","year":"2011","journal-title":"Int. J. Gynecol. Obstet."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e154","DOI":"10.7717\/peerj-cs.154","article-title":"Supervised deep learning embeddings for the prediction of cervical cancer diagnosis","volume":"4","author":"Fernandes","year":"2018","journal-title":"PeerJ Comput. Sci."},{"key":"ref_19","first-page":"S30","article-title":"Training for cervical cancer prevention programs in low-resource settings: Focus on visual inspection with acetic acid and cryotherapy","volume":"89","author":"Blumenthala","year":"2005","journal-title":"Int. J. Gynecol. Obstet."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4018\/IJHISI.2020040101","article-title":"Performance analysis of machine learning algorithms for cervical cancer detection","volume":"15","author":"Singh","year":"2020","journal-title":"Int. J. Healthc. Inf. Syst. Inform."},{"key":"ref_21","first-page":"1","article-title":"Classification of cervical neoplasms on colposcopic photography using deep learning","volume":"10","author":"Cho","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_22","unstructured":"Good, G. (2022, February 21). AI Approach Outperformed Human Experts in Identifying Cervical Precancer, Available online: https:\/\/www.nih.gov\/news-events\/news-releases\/ai-approach-outperformed-human-experts-identifying-cervical-precancer."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"16143","DOI":"10.1038\/s41598-021-95748-3","article-title":"Comparison of machine and deep learning for the classification of cervical cancer based on cervicography images","volume":"11","author":"Park","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_24","unstructured":"Rezvy, S., Zebin, T., Braden, B., Pang, W., Taylor, S., and Gao, X.W. (2020, January 3). Transfer learning for endoscopy disease detection & segmentation with mask-RCNN benchmark architecture. Proceedings of the 2nd International Workshop and Challenge on Computer Vision in Endoscopy, EndoCV@ISBI 2020, Iowa City, IA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"196160","DOI":"10.1109\/ACCESS.2020.3034367","article-title":"Accurate detection of septal defects with fetal ultrasonography images using deep learning-based multiclass instance segmentation","volume":"8","author":"Nurmaini","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Nurmaini, S., Rachmatullah, M.N., Sapitri, A.I., Darmawahyuni, A., Tutuko, B., Firdaus, F., Partan, R.U., and Bernolian, N. (2021). Deep learning-based computer-aided fetal echocardiography: Application to heart standard view segmentation for congenital heart defects detection. Sensors, 21.","DOI":"10.3390\/s21238007"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.1109\/TBME.2018.2887208","article-title":"Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, pocket colposcope","volume":"66","author":"Asiedu","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103163","DOI":"10.1016\/j.bspc.2021.103163","article-title":"HLDnet: Novel deep learning based artificial intelligence tool fuses acetic acid and Lugol\u2019s iodine cervicograms for accurate pre-cancer screening","volume":"1","author":"Yan","year":"2022","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104209","DOI":"10.1016\/j.compbiomed.2021.104209","article-title":"Diagnosis of cervical precancerous lesions based on multimodal feature changes","volume":"130","author":"Peng","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"11639","DOI":"10.1038\/s41598-020-68252-3","article-title":"The application of deep learning based diagnostic system to cervical squamous intraepithelial lesions recognition in colposcopy images","volume":"10","author":"Yuan","year":"2020","journal-title":"Sci. Rep."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5489\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:55:15Z","timestamp":1760140515000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5489"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,22]]},"references-count":30,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155489"],"URL":"https:\/\/doi.org\/10.3390\/s22155489","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,22]]}}}