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.
machine-learning computer-vision deep-learning cnn medical-imaging image-classification resnet darts automl resnet-50 neural-architecture-search medical-image-analysis ai-in-healthcare healthcare-ai cervical-cancer-detection nas-vs-resnet
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Updated
Mar 15, 2026 - Python