{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T13:01:37Z","timestamp":1789736497359,"version":"4.0.1"},"reference-count":17,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T00:00:00Z","timestamp":1722902400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns4:p>Background Medical imaging has seen significant advancements through machine learning, particularly convolutional neural networks (CNNs). These technologies have transformed the analysis of pathological images, enhancing the accuracy of diagnosing and classifying cellular anomalies. Digital pathology methodologies, including image analysis, have improved cervical cancer diagnostics. However, existing commercial platforms are often costly and restrictive, limiting customization and scalability. Methods CINNAMON-GUI is an open-source digital pathology tool based on CNNs for classifying Pap smear images. Transitioning to a Shiny app in Python, it offers enhanced user interface and interactivity. The application supports dynamic web interactions, advanced features for image analysis, and state-of-the-art CNN models tailored for digital pathology. Key features include intuitive UI components, real-time image and plot generation, memory-efficient data handling, and robust training capabilities with customizable CNN architectures. The tool also integrates with Labelme for defining regions of interest and allows testing on external biospecimens. Results Model A (seed 42, 100 epochs) and Model B (same architecture with adjusted augmentation parameters) were compared. Model A stabilized with training accuracy around 0.88 and validation accuracy around 0.913. Model B showed improved performance with training accuracy around 0.91 and validation accuracy around 0.95. Feature mapping highlighted critical morphological aspects, improving classification accuracy. Model B reduced misclassification errors significantly compared to Model A. Conclusions CINNAMON-GUI demonstrates the potential of an open-source platform in digital pathology, providing transparency and collaborative opportunities. The tool enhances diagnostic accuracy through feature map analysis and optimized CNN training. Future development aims to extend its application to other cancer types, leveraging its dynamic and user-friendly interface for broader use in diagnostics.<\/ns4:p>","DOI":"10.12688\/f1000research.154455.1","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T10:54:54Z","timestamp":1722941694000},"page":"897","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["CINNAMON-GUI: Revolutionizing Pap Smear Analysis with CNN-Based Digital Pathology Image Classification"],"prefix":"10.12688","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4348-6341","authenticated-orcid":false,"given":"Luca","family":"Zammataro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2560","published-online":{"date-parts":[[2024,8,6]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"209-249","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries.","volume":"71","author":"H Sung","year":"2021 May","journal-title":"CA Cancer J. Clin."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"22730-22736","DOI":"10.1073\/pnas.1911385116","article-title":"Whole-exome sequencing of cervical carcinomas identifies activating ERBB2 and PIK3CA mutations as targets for combination therapy.","volume":"116","author":"L Zammataro","year":"2019 Nov","journal-title":"Proc. Natl. Acad. Sci. USA."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"12-19","DOI":"10.1002\/(SICI)1096-9896(199909)189:1<12::AID-PATH431>3.0.CO;2-F","article-title":"Human papillomavirus is a necessary cause of invasive cervical cancer worldwide.","volume":"189","author":"J Walboomers","year":"1999 Sep","journal-title":"J. Pathol."},{"key":"ref4","doi-asserted-by":"publisher","first-page":"193-206","DOI":"10.1016\/S0002-9378(16)40621-6","article-title":"The Diagnostic Value of Vaginal Smears in Carcinoma of the Uterus**This study has been aided by the Commonwealth Fund. Presented before the New York Obstetrical Society, March 11, 1941.","volume":"42","author":"G Papanicolaou","year":"1941","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"123-127","DOI":"10.1002\/cncy.21346","article-title":"Evidence for increasing usage of low-grade squamous intraepithelial lesion, cannot exclude high-grade squamous intraepithelial lesion (LSIL-H) Pap test interpretations.","volume":"122","author":"V Walavalkar","year":"2014 Feb","journal-title":"Cancer Cytopathol."},{"key":"ref7","doi-asserted-by":"publisher","first-page":"9676206-9676208","DOI":"10.1155\/2023\/9676206","article-title":"Privacy Preserved Cervical Cancer Detection Using Convolutional Neural Networks Applied to Pap Smear Images.","volume":"2023","author":"S Alsubai","year":"2023","journal-title":"Comput. Math. Methods Med."},{"key":"ref8","doi-asserted-by":"publisher","first-page":"104649","DOI":"10.1016\/j.compbiomed.2021.104649","article-title":"DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques.","volume":"136","author":"M Rahaman","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref9","first-page":"3144-3148","article-title":"Sipakmed: A New Dataset for Feature and Image Based Classification of Normal and Pathological Cervical Cells in Pap Smear Images.","author":"M Plissiti","year":"2018"},{"key":"ref10","article-title":"Development team: Shiny for Python.","year":"2024"},{"key":"ref11","article-title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems.","author":"M Abadi","year":"2015"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"357-362","DOI":"10.1038\/s41586-020-2649-2","article-title":"Array programming with NumPy.","volume":"585","author":"C Harris","year":"2020 Sep","journal-title":"Nature."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"261-272","DOI":"10.1038\/s41592-019-0686-2","article-title":"SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python.","volume":"17","author":"P Virtanen","year":"2020","journal-title":"Nat. Methods."},{"key":"ref15","first-page":"2825-2830","article-title":"Scikit-learn: Machine learning in Python.","volume":"12","author":"F Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref16","article-title":"Labelme: Image Polygonal Annotation with Python.","author":"K Wada"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"682-688","DOI":"10.2353\/ajpath.2008.080280","article-title":"Koilocytosis: a cooperative interaction between the human papillomavirus E5 and E6 oncoproteins.","volume":"173","author":"E Krawczyk","year":"2008","journal-title":"Am. J. Pathol."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"1351-1356","DOI":"10.1038\/sj.bjc.6605328","article-title":"Koilocytes indicate a role for human papilloma virus in breast cancer.","volume":"101","author":"J Lawson","year":"2009","journal-title":"Br. J. Cancer."},{"key":"ref19","doi-asserted-by":"publisher","first-page":"331-338","DOI":"10.3349\/ymj.2014.55.2.331","article-title":"Diagnostic Algorithm to Reflect Regressive Changes of Human Papilloma Virus in Tissue Biopsies.","volume":"55","author":"M Lhee","year":"2014","journal-title":"Yonsei Med. J."}],"container-title":["F1000Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/f1000research.com\/articles\/13-897\/v1\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/13-897\/v1\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/f1000research.com\/articles\/13-897\/v1\/iparadigms","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,2]],"date-time":"2025-01-02T05:35:28Z","timestamp":1735796128000},"score":1,"resource":{"primary":{"URL":"https:\/\/f1000research.com\/articles\/13-897\/v1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,6]]},"references-count":17,"URL":"https:\/\/doi.org\/10.12688\/f1000research.154455.1","relation":{"has-review":[{"id-type":"doi","id":"10.5256\/f1000research.169489.r312178","asserted-by":"subject"},{"id-type":"doi","id":"10.5256\/f1000research.169489.r349037","asserted-by":"subject"},{"id-type":"doi","id":"10.5256\/f1000research.169489.r349037","asserted-by":"object"},{"id-type":"doi","id":"10.5256\/f1000research.169489.r312178","asserted-by":"object"}]},"ISSN":["2046-1402"],"issn-type":[{"value":"2046-1402","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,6]]},"assertion":[{"value":"Indexed","URL":"https:\/\/f1000research.com\/articles\/13-897\/v1#article-reports","order":0,"name":"referee-status","label":"Referee status","group":{"name":"current-referee-status","label":"Current Referee Status"}},{"value":"10.5256\/f1000research.169489.r312178, Fabio Grizzi, Humanitas University, IRCCS Humanitas Research Hospital, Rozzano, 09 Oct 2024, version 1, indexed","URL":"https:\/\/f1000research.com\/articles\/13-897\/v1#referee-response-312178","order":0,"name":"referee-response-312178","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.169489.r349037, Gabriella Casalino, University of Bari, Bari, 02 Jan 2025, version 1, indexed","URL":"https:\/\/f1000research.com\/articles\/13-897\/v1#referee-response-349037","order":1,"name":"referee-response-349037","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"The author(s) declared that no grants were involved in supporting this work","order":2,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}