Abstract
Women’s cancers remain a major challenge for many health systems. Between 1991 and 2017, the death rate for all major cancers fell continuously in the United States, excluding uterine cervix and uterine corpus cancers. Together with HPV (Human Papillomavirus) testing and cytology, colposcopy has played a central role in cervical cancer screening. This medical procedure allows physicians to view the cervix at a magnification of up to 10%. This paper presents an automated colposcopy image analysis framework for the classification of precancerous and cancerous lesions of the uterine cervix. This framework is based on an ensemble of MobileNetV2 networks. Our experimental results show that this method achieves accuracies of 83.33% and 91.66% on the four-class and binary classification tasks, respectively. These results are promising for the future use of automatic classification methods based on deep learning as tools to support medical doctors.
Highlights
It is estimated that every year, at least two million women are diagnosed with breast or cervical cancer [1]
In [1], it was estimated that approximately 85% of women diagnosed and 88% of women who die from cervical cancer live in an low and middle-income countries (LMIC)
We propose an ensemble of MobileNetV2 [32] networks as a solution to the problem of colposcopy images’ classification
Summary
It is estimated that every year, at least two million women are diagnosed with breast or cervical cancer [1]. Cervical cancer is the fourth most common women’s cancer worldwide, both in incidence and mortality, while it is the most common cancer in 38 countries [1]. Global inequities (both geographical and socio-economical) in cervical cancer incidence and mortality have long been observed [2], and they persist to this day. Women’s cancers are still a major challenge for global healthcare, especially in low and middle-income countries (LMIC) where approximately. In [1], it was estimated that approximately 85% of women diagnosed and 88% of women who die from cervical cancer live in an LMIC. A large proportion of cervical cancers in Ethiopia are diagnosed at an advanced stage [3]
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