A deep learning approach for Cervical Automated Risk Assessment (CARE) using images from a low-cost, portable Pocket colposcope

Erica Skerrett; Zichen Miao; Mercy N. Asiedu; Qiang Qiu; Mercy Nyamewaa Asiedu; Guillermo Sapiro; Nimmi Ramanujam

doi:10.1117/12.2608935

7 March 2022 A deep learning approach for Cervical Automated Risk Assessment (CARE) using images from a low-cost, portable Pocket colposcope

Erica Skerrett, Zichen Miao, Mercy N. Asiedu, Qiang Qiu, Mercy Nyamewaa Asiedu, Guillermo Sapiro, Nimmi Ramanujam

Author Affiliations +

Proceedings Volume PC11950, Optics and Biophotonics in Low-Resource Settings VIII; PC119500D (2022) https://doi.org/10.1117/12.2608935
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

For cervical cancer screening in low-HDI countries, the WHO recommends that a diagnosis is made immediately upon cervical visualization. To address variability in provider visual interpretations, we use CNNs to classify images from a low-cost, FDA-certified, portable Pocket colposcope images positive for high-grade precancer from a triaged population. We show that the combination of white-light acetic acid and green-light image stacks improves the AUC to 0.9. Pocket CARE can be used at the community level without the need for specialized physicians or inaccessible equipment, broadening access to early detection and treatment of pre-cursor lesions before they advance to cancer.

Conference Presentation

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Erica Skerrett, Zichen Miao, Mercy N. Asiedu, Qiang Qiu, Mercy Nyamewaa Asiedu, Guillermo Sapiro, and Nimmi Ramanujam "A deep learning approach for Cervical Automated Risk Assessment (CARE) using images from a low-cost, portable Pocket colposcope", Proc. SPIE PC11950, Optics and Biophotonics in Low-Resource Settings VIII, PC119500D (7 March 2022); https://doi.org/10.1117/12.2608935

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