A Proof of Concept Imaging System for Automated Cervical Cancer Screening in Peru
Raza Garcia, Mabel Karel
MetadataShow full item record
Cervical cancer is the second most frequent cancer in women around the world and affects half a million women per year. The World Health Organization (WHO) estimates that 275,000 women die every year, and 80% to 85% of these deaths occur in low-resource countries in Africa and South America. In Peru, cervical cancer has the highest incidence and the second highest mortality rates of cancers among women. Currently, the screening techniques such as the Papanicolau (Pap) test, in which some cells from the cervix are examined under a microscope to detect potentially pre-cancerous and cancerous cells, and the Visual Inspection with Acetic Acid (VIA), in which the surface layer of the cervix is examined through visual inspection after washing it with 3% to 5% acetic acid (vinegar) for one minute, are part of the national health policy in Peru. The Pap test is mainly used in urban areas in Peru. However, there are some challenges related to spreading the Pap test throughout the whole country: lack of quality and standardization of the readings of Pap smears, shortage of trained personnel, uneven processing of samples resulting in diagnosis and treatment delays, and lack of even basic laboratory infrastructure, all of which impacts greatly on the sustainability of this procedure in remote and/or poor settings. Extensive research has shown that computational solutions are a viable and suitable aid for overcoming these barriers. However, the majority of these solutions are commercial products that are not affordable for developing countries, such as Peru. In this context, developing a strategy, algorithm and open source computational implementation that recognizes normal vs. abnormal Pap smears can ultimately provide a cost-effective alternative for developing countries. The dissertation-specific objectives are to: 1) determine the characteristics of normal vs. abnormal Pap smears through expert consultation and relevant literature, 2) collect Pap smear data sets and run preliminary experiments to compare two pattern recognition algorithms in terms of features and classification performance, and 3) assess the accuracy, sensitivity and specificity of the proposed cervical cancer screening approach for classifying normal vs. abnormal Pap smears compared to experts' review.