Barriers to and facilitators of prostate cancer screening among men in Uganda prisons.

Background: Studies have shown that prostate cancer (PCa) is increasing at a rate of 5.2% per annum in Uganda and as few as 5% of men have ever been screened for PCa in Uganda. The situation may be worse among male prisoners given their 'vulnerable status'. The goal of this study was to examine the perceptions, attitudes and beliefs of men in Ugandan prisons regarding barriers to and facilitators of PCa screening. This would enable the identification of potential interventional strategies to promote PCa screening among men in Ugandan prisons. Methods: This study applied the explanatory sequential mixed methods study design. We first conducted 20 focus group discussions and 17 key informant interviews. The qualitative data were analysed to enrich a survey among 2,565 prisoners selected using a simple random sampling technique. Results: Qualitatively, the belief that all cancers have no cure was a barrier against most participants considering screening to be of any value, coupled with the fear of screening positive for PCa and the associated stress. In addition, poor PCa knowledge and lack of PCa screening services in prisons were perceived as barriers to PCa screening in prison settings.The quantitative data from the survey of 2,565 participants with a mean age of 50.2 (9.8), indicated that the main barriers to PCa screening were mainly myths, beliefs, lack of screening facilities and technical capacity. The majority believed that creating PCa awareness, conducting screening outreach in prisons, and providing equipment for PCa screening in prisons health facilities will facilitate PCa screening, as well as working with the Uganda prison service to train the prison health staff to perform PCa screen to facilitate Prison Health Centres capacity to screen for PCa. Conclusion: There is a need to develop interventions to increase awareness among the inmates in the prison health system, while ensuring that the prison health facilities are equipped with the required screening logistics, backed with outreaches from cancer-specialised hospitals/facilities.

A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images.

BACKGROUND: Cervical cancer is preventable if effective screening measures are in place. Pap-smear is the commonest technique used for early screening and diagnosis of cervical cancer. However, the manual analysis of the pap-smears is error prone due to human mistake, moreover, the process is tedious and time-consuming. Hence, it is beneficial to develop a computer-assisted diagnosis tool to make the pap-smear test more accurate and reliable. This paper describes the development of a tool for automated diagnosis and classification of cervical cancer from pap-smear images. METHOD: Scene segmentation was achieved through a Trainable Weka Segmentation classifier and a sequential elimination approach was used for debris rejection. Feature selection was achieved using simulated annealing integrated with a wrapper filter, while classification was achieved using a fuzzy C-means algorithm. RESULTS: The evaluation of the classifier was carried out on three different datasets (single cell images, multiple cell images and pap-smear slide images from a pathology lab). Overall classification accuracy, sensitivity and specificity of '98.88%, 99.28% and 97.47%', '97.64%, 98.08% and 97.16%' and '95.00%, 100% and 90.00%' were obtained for each dataset, respectively. The higher accuracy and sensitivity of the classifier was attributed to the robustness of the feature selection method that accurately selected cell features that improved the classification performance and the number of clusters used during defuzzification and classification. Results show that the method outperforms many of the existing algorithms in sensitivity (99.28%), specificity (97.47%), and accuracy (98.88%) when applied to the Herlev benchmark pap-smear dataset. False negative rate, false positive rate and classification error of 0.00%, 10.00% and 5.00%, respectively were obtained when applied to pap-smear slides from a pathology lab. CONCLUSIONS: The major contribution of this tool in a cervical cancer screening workflow is that it reduces on the time required by the cytotechnician to screen very many pap-smears by eliminating the obvious normal ones, hence more time can be put on the suspicious slides. The proposed system has the capability of analyzing a full pap-smear slide within 3 min as opposed to the 5-10 min per slide in the manual analysis. The tool presented in this paper is applicable to many pap-smear analysis systems but is particularly pertinent to low-cost systems that should be of significant benefit to developing economies.

A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images.

BACKGROUND AND OBJECTIVE: Early diagnosis and classification of a cancer type can help facilitate the subsequent clinical management of the patient. Cervical cancer ranks as the fourth most prevalent cancer affecting women worldwide and its early detection provides the opportunity to help save life. To that end, automated diagnosis and classification of cervical cancer from pap-smear images has become a necessity as it enables accurate, reliable and timely analysis of the condition's progress. This paper presents an overview of the state of the art as articulated in prominent recent publications focusing on automated detection of cervical cancer from pap-smear images. METHODS: The survey reviews publications on applications of image analysis and machine learning in automated diagnosis and classification of cervical cancer from pap-smear images spanning 15 years. The survey reviews 30 journal papers obtained electronically through four scientific databases (Google Scholar, Scopus, IEEE and Science Direct) searched using three sets of keywords: (1) segmentation, classification, cervical cancer; (2) medical imaging, machine learning, pap-smear; (3) automated system, classification, pap-smear. RESULTS: Most of the existing algorithms facilitate an accuracy of nearly 93.78% on an open pap-smear data set, segmented using CHAMP digital image software. K-nearest-neighbors and support vector machines algorithms have been reported to be excellent classifiers for cervical images with accuracies of over 99.27% and 98.5% respectively when applied to a 2-class classification problem (normal or abnormal). CONCLUSION: The reviewed papers indicate that there are still weaknesses in the available techniques that result in low accuracy of classification in some classes of cells. Moreover, most of the existing algorithms work either on single or on multiple cervical smear images. This accuracy can be increased by varying various parameters such as the features to be extracted, improvement in noise removal, using hybrid segmentation and classification techniques such of multi-level classifiers. Combining K-nearest-neighbors algorithm with other algorithm(s) such as support vector machines, pixel level classifications and including statistical shape models can also improve performance. Further, most of the developed classifiers are tested on accurately segmented images using commercially available software such as CHAMP software. There is thus a deficit of evidence that these algorithms will work in clinical settings found in developing countries (where 85% of cervical cancer incidences occur) that lack sufficient trained cytologists and the funds to buy the commercial segmentation software.