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Improving Mobile-Based Cervical Cytology Screening: A Deep Learning Nucleus-Based Approach for Lesion Detection

2023-08-31Journal article Open access
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dc.contributor.authorMosiichuk, Vladyslav
dc.contributor.authorSampaio, Ana
dc.contributor.authorViana, Paula
dc.contributor.authorOliveira, Tiago
dc.contributor.authorRosado, Luís
dc.date.accessioned2024-01-29T08:25:05Z
dc.date.available2024-01-29T08:25:05Z
dc.date.issued2023-08-31
dc.description.abstractLiquid-based cytology (LBC) plays a crucial role in the effective early detection of cervical cancer, contributing to substantially decreasing mortality rates. However, the visual examination of microscopic slides is a challenging, time-consuming, and ambiguous task. Shortages of specialized staff and equipment are increasing the interest in developing artificial intelligence (AI)-powered portable solutions to support screening programs. This paper presents a novel approach based on a RetinaNet model with a ResNet50 backbone to detect the nuclei of cervical lesions on mobile-acquired microscopic images of cytology samples, stratifying the lesions according to The Bethesda System (TBS) guidelines. This work was supported by a new dataset of images from LBC samples digitalized with a portable smartphone-based microscope, encompassing nucleus annotations of 31,698 normal squamous cells and 1395 lesions. Several experiments were conducted to optimize the model’s detection performance, namely hyperparameter tuning, transfer learning, detected class adjustments, and per-class score threshold optimization. The proposed nucleus-based methodology improved the best baseline reported in the literature for detecting cervical lesions on microscopic images exclusively acquired with mobile devices coupled to the μSmartScope prototype, with per-class average precision, recall, and F1 scores up to 17.6%, 22.9%, and 16.0%, respectively. Performance improvements were obtained by transferring knowledge from networks pre-trained on a smaller dataset closer to the target application domain, as well as including normal squamous nuclei as a class detected by the model. Per-class tuning of the score threshold also allowed us to obtain a model more suitable to support screening procedures, achieving F1 score improvements in most TBS classes. While further improvements are still required to use the proposed approach in a clinical context, this work reinforces the potential of using AI-powered mobile-based solutions to support cervical cancer screening. Such solutions can significantly impact screening programs worldwide, particularly in areas with limited access and restricted healthcare resources.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citation4. Mosiichuk V, Sampaio A, Viana P, Oliveira T, Rosado L. (2023). Improving Mobile-Based Cervical Cytology Screening: A Deep Learning Nucleus-Based Approach for Lesion Detection. Applied Sciences. 2023; 13(17):9850. https://doi.org/10.3390/app13179850pt_PT
dc.identifier.doi10.3390/app13179850pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/24732
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.subjectartificial intelligence; machine learning; deep learning; cervical cancer; cervical cytology; nucleus detection; lesion detection; computer-aided diagnosis; mobile devicept_PT
dc.titleImproving Mobile-Based Cervical Cytology Screening: A Deep Learning Nucleus-Based Approach for Lesion Detectionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue17pt_PT
oaire.citation.startPage9850pt_PT
oaire.citation.titleApplied Sciencespt_PT
oaire.citation.volume13pt_PT
person.familyNameMosiichuk
person.familyNameViana
person.familyNameCaeiro Margalho Guerra Rosado
person.givenNameVladyslav
person.givenNamePaula
person.givenNameLuís Filipe
person.identifier936138
person.identifier7dIAOMEAAAAJ&hl
person.identifier.ciencia-idEA17-B097-BD2E
person.identifier.ciencia-id6510-C0BA-7BD2
person.identifier.orcid0000-0001-7100-519X
person.identifier.orcid0000-0001-8447-2360
person.identifier.orcid0000-0002-8060-831X
person.identifier.scopus-author-id7003678537
person.identifier.scopus-author-id35604588400
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication201a2f59-1589-47c9-aac7-a3fcedbf4a04
relation.isAuthorOfPublication17ac1586-7589-4027-a541-3aea351fd6ae
relation.isAuthorOfPublication07f618c5-9549-4102-9940-ccd95ed38e65
relation.isAuthorOfPublication.latestForDiscovery201a2f59-1589-47c9-aac7-a3fcedbf4a04

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