An AI project that uses Differentiable Architecture Search (DARTS) to automatically design an optimized CNN for cervical cancer cell classification using the SIPaKMeD dataset. Compares the NAS-discovered model against a ResNet baseline across accuracy, F1-score, model size, inference time, and visualizations like confusion matrices and ROC curves.

In this project, we propose a cervical cancer detection and classification system using CNNs . We employ transfer learning and fine-tuning for enhanced performance. Classifiers like ELM and AE are added to increase the efficiency.

No description available

No description available

No description available

Classification of Cervical Cancer from snapshots of different classes

Cervical cancer type classification using cnn

Cervical cancer cell classification using deep learning CNN

CNN-based Image Classification Model on pap smear images to detect and classify Cervical Cancer, accurately upto 95 % using rank based Ensemble, integrated into a GUI Application.