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Publication
Improving Mobile-Based Cervical Cytology Screening: A Deep Learning Nucleus-Based Approach for Lesion Detection
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Advisor(s)
Abstract(s)
Liquid-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.
Description
Keywords
artificial intelligence; machine learning; deep learning; cervical cancer; cervical cytology; nucleus detection; lesion detection; computer-aided diagnosis; mobile device
Citation
4. 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/app13179850