| dc.description.abstract | Cervical cancer is the malignant tumor of the cervix. Cervical cancer remains one of the
gravest threats to women world wide. As cervical cancer is a highly preventable disease,
therefore, early screening represents the most effective strategy to reduce on the global burden
of cervical cancer. Commonly used cervical cancer screening methods are pap smear, visual
inspection with acetic acid, and colposcopy; however, these methods rely on the clincian’s
visual inspection for diagnosis. Due to scarce awareness, lack of access to medical centers,
and highly expensive procedure procedures in developing countries, the vulnerable patient
population cannot afford to undergo examination regularly. Additionally, there is small
number of oncologists in developing countries in relation to cancer cases.
The application of machine learning more specifically computer vision in the medical field
has proven to be less tedious, less time consuming and provides accurate diagnosis. Some of
the use cases of computer vision in the medical field are radiology, oncology, dermatology and
laboratory test automation.
Several computer vision based studies have been done to aid the screening of cervical cancer;
Ashutosh Kanitkar [3] proposed a novel deep learning approach to perform binary and multiclass
classification for cervical abnormality detection however; this was limited by small data and
no deployment was done.
In this research project, we implemented a deep learning model to aid in the screening of
cervical cancer. In addition to the cervical cancer screening task, we also implemented a deep
learning model to detect and classify various cervix types that is; Type 1, Type 2 and Type
3. We achieved an mAP of 0.879 for the cervical cancer screening task and an mAP of 0.646
for the cervix type detection task. We were able to deploy the models for both tasks unto an
Android smartphone. | en_US |
