Translational Medicine
Open Access
ISSN: 2161-1025
ISSN: 2161-1025
Alex Mirugwe* and Clare Ashaba
Introduction: Cervical cancer is the fourth most prevalent cancer among women worldwide and is a significant contributor to cancer- related deaths, with an estimated 300,000 women losing their lives to the disease annually. Most of these fatalities occur in Low and Middle Income Countries (LMICs), such as Uganda, where access to screening and treatment options is limited. Early detection of cervical cancer is crucial to improve the chances of survival for patients. Currently, cervical cancer screening is typically performed through pap smears, which involve manual examination of cervical samples for abnormalities by medical experts. This process is costly, time-consuming and prone to errors, leading to inaccurate diagnoses. Therefore, it is essential to find more effective and efficient alternative methods for cervical cancer screening to improve access in LMICs and alleviate the burden of cervical cancer.
Objective: The purpose of this study is to develop an automated pre-cervical cancer screening algorithm to detect precancerous cervical lesions.
Materials and methods: We developed a cancer screening algorithm using a 21 layer deep-learning convolution neural network trained on a dataset of 2300 images collected from local sources and some obtained from Kaggle.
Results: The best-performing classifier had an Area Under Curve (AUC) of the accuracy of 91.37%, a precision of 88.80%, a recall of 94.69%, an F1 score of 91.65% and an AUC of 96.0%.
Conclusion: The development and implementation of automated pre-cervical cancer screening algorithms have the potential to revolutionize cervical cancer detection and contribute significantly to reducing the burden of the disease, particularly in resource-limited settings.
Published Date: 2023-12-18; Received Date: 2023-11-17