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BackgroundCervical 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.MethodScene 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.ResultsThe 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.ConclusionsThe 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.

Objectives The need for affordable and appropriate medical technologies for developing countries continues to rise as challenges such as inadequate energy supply, limited technical expertise, and poor infrastructure persist. Low-field magnetic resonance imaging (LF MRI) is a technology that can be tailored to meet specific imaging needs within such countries. Its low power requirements and the possibility of operating in minimally shielded or unshielded environments make it especially attractive. Although the technology has been widely demonstrated over several decades, it is yet to be shown that it can be diagnostic and improve patient outcomes in clinical applications. We here demonstrate the robustness of prepolarizing MRI (PMRI) technology for assembly and deployment in developing countries for the specific application to infant hydrocephalus. Hydrocephalus treatment planning and management requires only modest spatial resolution, such that the brain can be distinguished from fluid—tissue contrast detail within the brain parenchyma is not essential. Materials and Methods We constructed an internally shielded PMRI system based on the Lee-Whiting coil system with a 22-cm diameter of spherical volume. Results In an unshielded room, projection phantom images were acquired at 113 kHz with in-plane resolution of 3 mm × 3 mm, by introducing gradient fields of sufficient magnitude to dominate the 5000 ppm inhomogeneity of the readout field. Discussion The low cost, straightforward assembly, deployment potential, and maintenance requirements demonstrate the suitability of our PMRI system for developing countries. Further improvement in image spatial resolution and contrast of LF MRI will broaden its potential clinical utility beyond hydrocephalus.

Magnetic resonance imaging (MRI) technology has profoundly transformed current healthcare systems globally, owing to advances in hardware and software research innovations. Despite these advances, MRI remains largely inaccessible to clinicians, patients, and researchers in low‐resource areas, such as Africa. The rapidly growing burden of noncommunicable diseases in Africa underscores the importance of improving access to MRI equipment as well as training and research opportunities on the continent. The Consortium for Advancement of MRI Education and Research in Africa (CAMERA) is a network of African biomedical imaging experts and global partners, implementing novel strategies to advance MRI access and research in Africa. Upon its inception in 2019, CAMERA sets out to identify challenges to MRI usage and provide a framework for addressing MRI needs in the region. To this end, CAMERA conducted a needs assessment survey (NAS) and a series of symposia at international MRI society meetings over a 2‐year period. The 68‐question NAS was distributed to MRI users in Africa and was completed by 157 clinicians and scientists from across Sub‐Saharan Africa (SSA). On average, the number of MRI scanners per million people remained at less than one, of which 39% were obsolete low‐field systems but still in use to meet daily clinical needs. The feasibility of coupling stable energy supplies from various sources has contributed to the growing number of higher‐field (1.5 T) MRI scanners in the region. However, these systems are underutilized, with only 8% of facilities reporting clinical scans of 15 or more patients per day, per scanner. The most frequently reported MRI scans were neurological and musculoskeletal. The CAMERA NAS combined with the World Health Organization and International Atomic Energy Agency data provides the most up‐to‐date data on MRI density in Africa and offers a unique insight into Africa's MRI needs. Reported gaps in training, maintenance, and research capacity indicate ongoing challenges in providing sustainable high‐value MRI access in SSA. Findings from the NAS and focused discussions at international MRI society meetings provided the basis for the framework presented here for advancing MRI capacity in SSA. While these findings pertain to SSA, the framework provides a model for advancing imaging needs in other low‐resource settings.