Main scientific papers related to the PRESCRIP-TEC project
Cervical cancer screening and treatment in Uganda
Cervical cancer is the leading cause of cancer death among women in Uganda. Given the high prevalence of genital human papillomavirus infection, the current unavailability of radiotherapy, and the absence of a national cervical cancer prevention and control program, these deaths will likely increase. Read More
Training of health professionals, ongoing construction of new radiotherapy bunkers, and opening of regional centers are all geared towards improving cervical cancer care in Uganda. The Uganda Cancer Institute Bill establishes the Institute as a semi-autonomous agency mandated to undertake and coordinate the prevention and treatment of cancer. Its implementation will be a milestone in cervical cancer prevention and control. However, execution will require political will and an increase in domestic and international investment.
Hybrid Transfer Learning for Classification of Uterine Cervix Images for Cervical Cancer Screening
Transfer learning using deep pre-trained convolutional neural networks is increasingly used to solve a large number of problems in the medical field. In spite of being trained using images with entirely different domain, these networks are flexible to adapt to solve a problem in a different domain too. Read More
A very few features out of these features are useful for solving the problem in a different domain, while others are irrelevant, use of which may only reduce the efficacy of the network. However, by minimizing the number of filters required to solve the problem, the efficiency of the training the network can be improved. In this study, we consider identification of relevant filters using the pre-trained networks namely AlexNet and VGG-16 net to detect cervical cancer from cervix images. This paper presents a novel hybrid transfer learning technique, in which a CNN is built and trained from scratch, with initial weights of only those filters which were identified as relevant using AlexNet and VGG-16 net. This study used 2198 cervix images with 1090 belonging to negative class and 1108 to positive class. Our experiment using hybrid transfer learning achieved an accuracy of 91.46%.
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Integrated cervical cancer screening in
Mayuge District Uganda (ASPIRE Mayuge): a
pragmatic sequential cluster randomized
Cervical cancer is almost entirely preventable through vaccination and screening, yet remains one of the ‘gravest threats to women’s lives’ according to the World Health Organization. Specific high-risk subtypes of human papillomavirus (HR-HPV) are well-established as the primary cause of cervical cancer. Read More
Results from this study will inform the national scale-up of cervical cancer screening in Uganda, aligning with the World Health Organization’s target of achieving cervical cancer elimination through the pillar of increased HPV screening coverage.
Andriod Device-Based Cervical Cancer Screening for Resource-Poor Settings
Visual inspection with acetic acid (VIA) is an effective, affordable and simple test for cervical cancer screening in resource-poor settings. But considerable expertise is needed to differentiate cancerous lesions from normal lesions, which is lacking in developing countries. Many studies have attempted automation of cervical cancer detection from cervix images acquired during the VIA process. Read More
An android device with an inbuilt app to acquire images and provide instant results would be an obvious choice in resource-poor settings. In this paper, we propose an algorithm for analysis of cervix images acquired using an android device, which can be used for the development of decision support system to provide instant decision during cervical cancer screening. This algorithm offers an accuracy of 97.94%, a sensitivity of 99.05% and specificity of 97.16%.
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