THEAICOMP LTD

AI for Healthcare

My project focuses on applying deep learning techniques to improve the early detection and classification of cervical cancer. Cervical cancer remains a major health challenge, particularly in low-resource settings where access to skilled pathologists and diagnostic tools is limited. By leveraging advances in artificial intelligence—specifically within the field of Deep Learning—the project aims to build a system that can automatically analyze medical images, such as Pap smear or histopathology slides, and accurately identify cancerous or precancerous cells. This approach has the potential to support clinicians, reduce diagnostic errors, and increase screening efficiency. The core of the project involves training and evaluating multiple convolutional neural network (CNN) architectures on labeled cervical cell image datasets. These models learn to extract complex visual patterns and features that distinguish normal cells from abnormal ones. Through preprocessing steps such as image normalization, augmentation, and segmentation, the dataset is optimized to improve model performance and generalization. The project compares different model architectures based on key performance metrics such as accuracy, precision, recall, and F1-score, with the goal of identifying the most reliable model for real-world application.