AUTOMATED CLASSIFICATION OF CERVICAL CELL IMAGES FOR CANCER SCREENING

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