Abstract:
Cervical cancer remains a leading cause of mortality among women worldwide, with
multiple deaths annually. Effective screening programs that facilitate early detection
critical for improving outcomes and saving lives. The current standard relies
manual examination of Pap smear slides by skilledcytotechnologists, which is time-
consuming, costly, and prone to human error. This project proposes an automated
cervical cancer screening system using deep learning and computer vision techniques
to analyze cytology images. A convolutional neural network classifier will be
developed to categorize cervical cells extracted from whole slide images into normal,
and abnormal classes. The model will be trained on a dataset of Pap smeai imagery
with expert cell classifications. Extensive experiments will tune the architecture, hyper
parameters, and regularization strategies to optimize performance. Advanced
techniques including transfer learning and data augmentation will be employed to
and generalizability. Once validated, this artificial intelligence
on
enhance accuracy
powered screening assistant could greatly increase the speed, consistency, and
accessibilityof cervical cancer detection. By reliably flagging potentially
could be administered sooner, preventing disease progression. The
cancerous
cells, interventions
approach could be extended to improve outcomes for a variety of cancer types. This
project will demonstrate the profound impact that AI and machine learning can have
on life-saving medical diagnostics
