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CNN-based Approach for Cervical Cancer Classification in Whole-Slide Histopathology Images

Domaine:

healthcare

Type de record:

paper
Créateur:
IdlHimMah
Éditeur:
arXiv
Hôte:avatar
Cervical cancer will cause 460 000 deaths per year by 2040, approximately 90% are Sub-Saharan African women. A constantly increasing incidence in Africa making cervical cancer a priority by the World Health Organization (WHO) in terms of screening, diagnosis, and treatment. Conventionally, cancer diagnosis relies primarily on histopathological assessment, a deeply error-prone procedure requiring intelligent computer-aided systems as low-cost patient safety mechanisms but lack of labeled data in digital pathology limits their applicability. In this study, few cervical tissue digital slides from TCGA data portal were pre-processed to overcome whole-slide images obstacles and included in our proposed VGG16-CNN classification approach. Our results achieved an accuracy of 98,26% and an F1-score of 97,9%, which confirm the potential of transfer learning on this weakly-supervised task. Presented at the ICLR 2020 Workshop on AI for Overcoming Global Disparities in Cancer Care (AI4CC)

Visit

doi.orgarxiv.org

Tasks

computer visionimage classification

Tags

Computer Vision and Pattern Recognition (cs.CV)Image and Video Processing (eess.IV)FOS: Computer and information sciencesFOS: Computer and information sciencesFOS: Electrical engineering, electronic engineering, information engineeringFOS: Electrical engineering, electronic engineering, information engineering

Licenses

arXiv.org perpetual, non-exclusive licensehttp://arxiv.org/licenses/nonexclusive-distrib/1.0/

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