William Wasswa




The initial development of PapES was based on online cervical cell datasets to develop algorithms for segmentation, feature extraction and classification. Later, the tool was evaluated with pap-smear slides from the pathology unit at Mbarara Regional Referral Hospital. Under the department of Biomedical Sciences and Engineering, PapES, has been developed for automated diagnosis and classification of cervical cancer from pap-smear images but is particularly pertinent to resource-constrained areas and could be of significant benefit to developing economies. The tool also takes into consideration the patient’s cervical cancer risk factors. A cytopathologist analyses the patient’s cervical cancer risk factors and the tool generates a result on the possibility of cervical cancer. Subsequently, the cytopathologist can upload the pap-smear to segment the image using the developed techniques and extract cell features. The tool can then give the diagnosis and stage of cervical cancer, as per the cervix cell changes from the pap-smear. A low-cost automated digital microscope slide scanner has also been developed to acquire quick, reliable and high-resolution digital pap-smear images from the pap-smear slides for automated analysis. The tool will be complemented by the development of an AI empowered integrated cervical cancer patients’ information management expert system (CIMES). This system will be able to make predictions on the recurrence of cervical cancer based on available patient specific data.