This project presents an innovative hybrid artificial intelligence model developed for the automated detection and classification of abnormal cervical cells using Pap smear images.

Cervical cancer prediction via the SIPaKMeD dataset. Compares HOG handcrafted features vs. VGG16 deep learning. Uses KMeans & SVM to achieve 87% accuracy in Pap smear cell classification. Created for the Computational Vision course.

Habiba Omran Thesis

End-to-end AI workflow for cervical cytology using MobileViT and Cellpose. Features automated cell segmentation, classification on SIPaKMeD, and PDF clinical reporting via FastAPI.