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

• URL: http://arxiv.org/abs/2005.13924v1
• Date: Thu, 28 May 2020 11:45:23 GMT
• Title: CNN-based Approach for Cervical Cancer Classification in Whole-Slide Histopathology Images
• Authors: Ferdaous Idlahcen, Mohammed Majid Himmi, Abdelhak Mahmoudi
• Abstract summary: Cervical cancer will cause 460 000 deaths per year by 2040, approximately 90% are Sub-Saharan African women. Few cervical tissue digital slides from TCGA data portal were pre-processed to overcome whole-slide images obstacles. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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